Predicting the Jump: Decoding Viral Zoonotic Potential and Cross-Species Transmission

Abstract

This article synthesizes current research on the mechanisms and predictors of viral zoonotic potential for a scientific audience of researchers and drug development professionals. It explores the foundational evolutionary and ecological drivers of cross-species transmission, examines cutting-edge methodological approaches for risk assessment—including genomic surveillance and machine learning—and addresses the significant challenges in data gaps and therapeutic development. Finally, it validates these approaches through the lens of the One Health framework and comparative analysis of recent zoonotic threats, providing a comprehensive roadmap for proactive pandemic preparedness.

The Drivers of Spillover: Unpacking Viral Evolution and Host-Jump Ecology

Zoonotic potential refers to the inherent capacity of a pathogen circulating in animal populations to overcome a series of biological and ecological barriers, leading to successful infection of a human host. Understanding this potential is a critical frontier in public health, as the majority of emerging infectious diseases in humans are of animal origin. It is estimated that over 60% of known human pathogens are zoonotic, and a staggering 75% of emerging infectious diseases result from spillover events from animals [1] [2] [3]. The recent COVID-19 pandemic, caused by the SARS-CoV-2 virus of likely bat origin, is a stark reminder of the devastating global consequences of zoonotic spillover [1]. This guide provides a technical framework for researchers and drug development professionals, synthesizing current knowledge on the mechanisms, assessment, and surveillance of zoonotic potential within the broader context of viral zoonosis and species jump research.

The conceptual foundation for studying these events is the "One Health" approach, which recognizes the inextricable linkages between human, animal, and ecosystem health [4]. A holistic understanding of zoonotic potential requires integrating data from virology, ecology, veterinary science, and human medicine to effectively predict and prevent future pandemics.

Quantitative Landscape of Zoonotic Threats

Systematic surveillance and virus discovery efforts have begun to quantify the vast universe of viruses with unknown zoonotic risk. One study that tested over 509,721 samples from 74,635 animals created a database of 887 wildlife-origin viruses from 19 virus families [5]. The following table summarizes the distribution of major zoonotic pathogens and their primary animal reservoirs, providing a landscape of known threats.

Table 1: Major Zoonotic Pathogen Classes and Representative Diseases

Pathogen Type	Representative Diseases	Key Animal Reservoirs
Bacterial	Anthrax, Tuberculosis, Brucellosis, Plague, Leptospirosis, Salmonellosis	Cattle, sheep, rodents, goats, pigs, wildlife [1]
Viral	Rabies, Avian Influenza, Ebola, SARS, MERS, COVID-19, Nipah, Hantavirus	Bats, birds, primates, rodents, dogs [1] [6]
Parasitic	Trichinosis, Toxoplasmosis, Echinococcosis	Swine, cats, rodents, foxes, livestock [1]
Fungal	Ringworm	Various domestic and wild animals [1]

Risk ranking frameworks have been developed to systematically evaluate novel viruses. The SpillOver platform, for instance, uses 31 risk factors to generate a comparative risk score for wildlife-origin viruses, creating a watchlist of potential pathogens for targeted research and countermeasure development [5]. In one application, this tool ranked the spillover potential of 887 wildlife viruses, and validating its efficacy, the top 12 were known zoonotic viruses, including SARS-CoV-2 [5].

Table 2: Key Risk Factors for Zoonotic Spillover and Spread Potential

Risk Factor Category	Specific Factors	Influence on Spillover Risk
Virus-Related	Virus family, genetic similarity to known human pathogens, mode of transmission, environmental stability	Determines pathogen's inherent ability to infect human cells and survive between hosts [5] [7]
Host-Related	Reservoir host species, population density, prevalence of infection, shedding route (e.g., respiratory, urinary)	Influences the intensity and route of pathogen release into the environment [5] [7]
Environmental/Ecological	Frequency and intimacy of human-animal contact, land-use change (e.g., deforestation), climate change	Affects the probability of human exposure to the pathogen [5] [6]
Human-Related	Human behavior, susceptibility to infection, population density, immunity	Impacts the final step of establishing infection and potential for onward transmission [7]

Mechanisms of Zoonotic Spillover and Species Jump

Zoonotic spillover is not a single event but the culmination of a hierarchical series of processes that must align in space and time for a pathogen to jump from an animal to a human [7]. This process can be conceptualized as a pathway through a sequence of barriers.

The Hierarchical Spillover Pathway

The following diagram illustrates the sequential barriers a pathogen must overcome to cause a spillover infection.

Diagram 1: The Hierarchical Barrier Model of Zoonotic Spillover. This pathway visualizes the sequential phases a pathogen must pass through, from residing in an animal reservoir to establishing an infection in a human host. Critical bottlenecks exist at each barrier, and spillover can only occur when gaps align across all barriers [7].

Key Stages in the Spillover Process

• Barrier 1: Generating Pathogen Pressure: The process begins with "pathogen pressure," defined as the amount of infectious pathogen available at a point in space and time where humans could be exposed [7]. This pressure is determined by:

  • Dynamics in the Reservoir Host: The density of the animal host population, the prevalence of infection within that population, and the intensity of infection in individual hosts [7]. For example, pulses of Hantavirus in deer mice are driven by climate-influenced increases in rodent density [7].
  • Pathogen Release: The pathogen must be shed via a relevant route (e.g., respiratory droplets, saliva, feces, urine) and in sufficient quantities. For instance, rabies virus is released via saliva, while pathogenic Leptospira are shed in urine [7].
  • Environmental Survival and Dispersal: After release, the pathogen must survive in the environment (air, water, soil) long enough and disperse sufficiently to reach a human. The spread of aerosolized Coxiella burnetii by wind over several kilometers is a key example [7].

• Barrier 2: Human Exposure to the Pathogen: Even with high pathogen pressure, spillover requires a human to be exposed to an infectious dose. This phase is governed by:

  • Contact Points: Direct contact with infected animals (e.g., through hunting, farming), indirect contact (e.g., with contaminated soil or water), or vector-borne transmission (e.g., via mosquitoes or ticks) [2] [6].
  • Human Behavior: Occupational practices, culinary traditions, and recreational activities significantly influence exposure risk. Markets selling live wild animals are considered particularly high-risk interfaces [2].

• Barrier 3: Establishing Infection in the Human Host: The final barrier is within the human body. The pathogen must overcome host defenses to establish an infection. Key factors include:

  • Cellular Receptors: The pathogen must be able to use human cell receptors for entry. The compatibility between animal virus surface proteins and human receptors is a major determinant of infectivity [7].
  • Within-Host Susceptibility: The individual's genetic background, immune status, and physiology affect their susceptibility to infection [7].
  • Viral Fitness: The pathogen must be able to replicate within the human cellular environment. Some viruses may require adaptive mutations to achieve sustained replication [7].

Experimental and Surveillance Methodologies

Proactive surveillance and advanced laboratory characterization are essential for translating the theoretical framework of spillover into actionable data for pandemic prevention.

Field Surveillance and Metagenomics

Modern surveillance strategies employ metagenomic sequencing to comprehensively document viral diversity in wildlife populations. A recent large-scale study in China exemplifies this approach, sequencing samples from 2,175 individual animals (including bats, rodents, pangolins, and zoo animals) to identify 328 viruses, 171 of which had near-complete genomes assembled [8]. The experimental workflow for such studies is outlined below.

Diagram 2: Workflow for Wildlife Virus Surveillance and Identification. This protocol details the steps from biological sample collection to the confirmation of novel viruses, combining next-generation sequencing with phylogenetic and experimental validation [8].

This surveillance revealed complex transmission dynamics, such as the circulation of picornaviruses and respiroviruses between bats and pangolins, and cross-species transmission of paramyxoviruses and astroviruses between wildlife and domestic animals [8]. Such findings underscore the interconnectedness of ecosystems and the complexity of tracking zoonotic threats.

The Scientist's Toolkit: Key Research Reagent Solutions

The following table details essential reagents and materials used in the surveillance and characterization of zoonotic pathogens.

Table 3: Essential Research Reagents for Zoonotic Pathogen Studies

Research Reagent / Tool	Core Function	Application Example
Nucleic Acid Extraction Kits	Isolate RNA/DNA from diverse sample matrices (tissue, swabs, feces) for downstream sequencing and PCR.	Preparing meta-transcriptomic libraries from bat rectal swabs or pangolin lung tissue [8].
Pan-viral Consensus PCR Primers	Amplify broad groups of viruses (e.g., all coronaviruses, paramyxoviruses) for targeted discovery and detection.	Initial screening for novel coronaviruses in wildlife samples as part of the PREDICT project [5].
Next-Generation Sequencing Platforms	Conduct untargeted, high-throughput sequencing to identify known and novel pathogens without prior hypothesis.	Generating an average of 12 Gb of sequence data per library to characterize animal viromes [8].
Cell Culture Lines (e.g., Vero, BHK-21)	Propagate and isolate live viruses from clinical or animal samples for phenotypic characterization.	Isating and characterizing the pathogenicity of eight novel viruses discovered in surveillance [8].
Pathogen-Specific Antibodies	Detect viral antigens in tissues (immunohistochemistry) or cell culture (immunofluorescence) to confirm active replication.	Confirming virus protein expression in infected cell cultures during pathogenicity studies [8].
Plaque Assay Reagents	Quantify infectious viral particles in a sample (viral titer) to measure replication kinetics and infectious dose.	Determining the growth curves and replication efficiency of isolated viruses in different cell lines [8].
CDDO-dhTFEA	CDDO-dhTFEA, MF:C33H45F3N2O3, MW:574.7 g/mol	Chemical Reagent
Flt3-IN-3	Flt3-IN-3, MF:C27H38N8O, MW:490.6 g/mol	Chemical Reagent

Risk Assessment and Prioritization Frameworks

Given the vast number of undiscovered viruses, quantitative risk assessment frameworks are necessary to prioritize resources toward the most significant threats. The SpillOver tool uses a weighted analysis of 31 risk factors—covering virus, host, and environmental characteristics—to generate a comparative risk score for wildlife-origin viruses [5]. This approach is similar to a credit score for viral threats, enabling evidence-based prioritization.

Another methodology, Conjoint Analysis (CA), has been used to quantify the relative importance of key zoonotic disease characteristics from the perspective of health professionals [9]. This method forces trade-offs between criteria, revealing that factors like frequency of human-animal contact, mode of transmission, and severity of human disease are heavily weighted in expert assessments of priority [9]. These quantitative approaches move beyond subjective listing to provide a transparent, data-driven foundation for national disease prioritization, prevention, and control actions.

Defining zoonotic potential is a complex, multi-faceted challenge that requires integrating data across the hierarchical spillover pathway—from the dynamics in wildlife reservoirs to the final establishment of infection in a human. The frameworks and methodologies outlined in this guide provide a roadmap for researchers and drug developers to systematically assess and quantify this risk.

Future efforts must focus on enhancing international surveillance networks, such as the Global Early Warning System (GLEWS), and fostering open data sharing [2]. Deepening our understanding of viral genetics, host-pathogen interactions, and the ecological drivers of spillover, such as land-use change and wildlife trade, will be crucial [3] [6]. Ultimately, mitigating the risk of future pandemics depends on a sustained, collaborative, and well-funded "One Health" research agenda that proactively confronts the threat at the animal-human interface.

Viral host jumps represent a significant threat to global public health, food security, and biodiversity. While the ecological drivers of cross-species transmission have been extensively studied, the evolutionary mechanisms and genomic correlates underpinning these events remain less characterized. This whitepaper synthesizes findings from large-scale genomic analyses to elucidate the patterns of natural selection and genetic adaptation that enable viruses to overcome species barriers. We examine the directionality of host jumps, the extent of genomic reorganization, and the specific viral genes targeted by selection during spillover events. Our analysis reveals that humans act as a significant source of viruses for other animals, that host jumps are associated with accelerated evolution, and that the genomic targets of selection vary substantially across viral families. These insights provide a framework for predicting viral emergence and developing targeted interventions against future zoonotic threats.

The majority of emerging and re-emerging infectious diseases in humans are caused by viruses that have jumped from wild and domestic animal populations, a process known as zoonosis [10]. These cross-species transmission events can cause disease outbreaks, epidemics, and pandemics that exact a substantial toll on human health and global economies. Despite extensive research into the ecological risk factors facilitating zoonotic transmission, the evolutionary drivers and genomic correlates of viral host jumps have received comparatively less attention until recently [10] [11].

Characterizing the evolutionary processes that enable viruses to successfully establish infections in new host species is critical for predicting emergence events and developing effective countermeasures. This technical review synthesizes current understanding of how viruses evolve to cross species barriers, with particular emphasis on genomic signatures of adaptation, the relative frequency of different transmission directions, and methodological approaches for investigating these phenomena. The findings presented herein are based primarily on analyses of nearly 12 million viral sequences available through public databases, enabling unprecedented insights into the macroevolutionary patterns of viral host jumps [10] [11].

The Genomic Landscape of Viral Host Jumps

Directionality of Cross-Species Transmission

Conventional perspectives on viral host jumps have largely framed humans as recipients of animal viruses, with far less attention paid to reverse transmission events. However, recent analysis of viral genomic data has revealed a more complex pattern of viral exchange across the animal kingdom.

Table 1: Directionality of Viral Host Jumps Based on Genomic Analysis

Transmission Direction	Relative Frequency	Key Observations	Implications
Human-to-animal (anthroponosis)	Approximately twice as frequent as zoonosis [12]	Consistent pattern across most viral families studied [10]	Humans are a significant viral source; conservation and food security impacts
Animal-to-human (zoonosis)	Less frequent than anthroponosis [10]	Primary focus of most emerging disease research	Continued surveillance remains critical for pandemic prevention
Animal-to-animal	Most frequent transmission pathway [10]	Does not directly involve human hosts	Forms complex ecological network with potential indirect human health impacts

This analysis positions humans as "just one node in a vast network of hosts endlessly exchanging pathogens, rather than a sink for zoonotic bugs" [12]. The high frequency of human-to-animal transmission (anthroponosis) has important implications for conservation biology, as viruses transmitted from humans to wildlife can threaten endangered species and ecosystem stability [10]. Additionally, such transmission events may establish animal reservoirs for human viruses, creating potential sources for re-emergence in human populations following further viral adaptation [10].

Genomic Signatures of Host Jump Adaptation

Viral host jumps are associated with distinct genomic signatures that reflect the evolutionary pressures encountered when adapting to new host species. Analysis of viral sequences before, during, and after cross-species transmission events has revealed several consistent patterns of genetic adaptation.

Table 2: Genomic Correlates of Viral Host Jumps

Evolutionary Parameter	Observation	Interpretation
Evolutionary rate	Heightened evolution in viral lineages involving host jumps [10] [11]	Adaptive evolution to overcome host-specific barriers
Extent of adaptation	Lower for viruses with broader host ranges [10] [11]	Generalist viruses pre-adapted to multiple hosts require fewer changes
Genomic targets of selection	Varies by viral family; either structural or auxiliary genes prime targets [10]	Different viral families employ distinct adaptive strategies
Cell entry proteins	Often not the primary target of adaptive mutations [12]	Host adaptation involves complex processes beyond receptor binding

The finding that viruses with broader host ranges exhibit less extensive adaptation during host jumps suggests that generalist viruses possess inherent traits that facilitate infection of diverse hosts [10] [12]. Conversely, viruses with narrower host ranges may require more substantial genetic reorganization to successfully establish infections in new species. Interestingly, the observation that cell entry proteins are frequently not the primary targets of selection indicates that viral host adaptation involves complex processes beyond initial attachment and entry, potentially including immune evasion, intracellular replication, and transmission efficiency [12].

Methodological Approaches for Studying Host Jump Evolution

Large-Scale Genomic Analysis

Comprehensive investigation of viral host jump evolution requires methodological approaches capable of processing and analyzing the enormous volume of available genomic data. The most influential recent studies have employed sophisticated computational pipelines that integrate multiple analytical techniques.

Experimental Protocol 1: Genomic Analysis of Host Jumps

• Data Acquisition: Download all available viral sequences and associated metadata from public databases (e.g., NCBI Virus). Current studies have utilized ~12 million viral sequences [10] [11].

• Quality Control and Filtering:

  • Retain only high-quality complete genomes
  • Exclude sequences with poor metadata annotation
  • Focus on vertebrate-associated viruses from targeted viral families

• Viral Clique Definition: Implement species-agnostic network analysis to define "viral cliques" - discrete taxonomic units with similar genetic diversity. This approach effectively partitions genomic diversity into biologically relevant units that may not align perfectly with established taxonomic classifications [10].

• Host Jump Identification:

  • Generate curated whole-genome alignments for each viral clique
  • Apply maximum-likelihood phylogenetic reconstruction
  • For segmented viruses, use single-gene alignments due to high reassortment frequency
  • Reconstruct ancestral states to infer cross-species transmission events [10]

• Adaptation Analysis:

  • Identify genomic regions with elevated evolutionary rates
  • Detect signatures of positive selection in viral genes
  • Correlate genetic changes with host jump events

Phylogenetic Factorization for Epidemic Potential Assessment

For assessing the distribution of viral epidemic potential across host species, phylogenetic factorization approaches provide a flexible framework for identifying clades with unusually high or low propensity to harbor virulent viruses.

Experimental Protocol 2: Assessing Clade-Specific Viral Epidemic Potential

• Host-Virus Association Data: Extract mammal-virus associations from comprehensive databases (e.g., Global Virome in One Network - VIRION) [13].

• Epidemic Potential Metrics:

  • Calculate case-fatality rates (CFR) for each virus
  • Determine transmissibility (human-to-human transmission capability)
  • Compute death burden (total human mortality since 1950)
  • Average metrics across all viruses per host species

• Phylogenetic Signal Analysis:

  • Estimate phylogenetic signal using Pagel's λ and Blomberg's K
  • Compare observed variation to Brownian motion expectation
  • Assess evolutionary conservation of epidemic potential traits

• Phylogenetic Factorization:

  • Iteratively partition phylogeny to identify nodes with maximum contrast in response variables
  • Use generalized linear models (GLMs) for each phylogenetic edge
  • Apply multiple comparison corrections (e.g., Holm's sequentially rejective test)
  • Control for sampling effort using virus count per host as precision covariate [13]

This approach has revealed that viral epidemic potential is not uniformly distributed across host taxa but clusters within specific clades. For example, within bats, only certain phylogenetic groups harbor viruses with high virulence in humans, rather than the entire order exhibiting equal zoonotic risk [13].

Case Studies in Viral Host Jump Evolution

Bat-Borne Viruses and Epidemic Potential

Bats (order Chiroptera) have received significant attention as reservoir hosts for numerous high-impact zoonotic viruses. Recent research has demonstrated that bats harbor more viruses with high virulence in humans than other mammalian or avian orders [13]. However, contrary to common perception, this risk is not uniformly distributed across the bat phylogeny.

Table 3: Distribution of High-Epidemic Potential Viruses in Bats

Bat Group	Viral Epidemic Potential	Geographic Hotspots	Notable Viral Associations
Specific clades with high virulence viruses	Concentrated in particular phylogenetic groups	Coastal South America, Southeast Asia, Equatorial Africa [13]	SARS-like coronaviruses, Nipah virus, Hendra virus
Cosmopolitan families	Overrepresented among high-risk clades [13]	Multiple regions globally	Various coronaviruses, paramyxoviruses
Remaining bat species	Lower virulence viral communities	Distributed across all bat habitats	Mostly low-impact or unknown human pathogens

The uneven distribution of high-virulence viruses across bat species underscores the importance of targeted surveillance rather than blanket approaches to bat-associated zoonotic risk. This refined understanding can help focus resources on the specific bat groups and geographic regions posing the greatest potential threat, while simultaneously promoting more nuanced public perceptions of bats that recognize their ecological importance beyond being disease reservoirs [13].

Machine Learning Approaches for Therapeutic Development

The identification of potential treatments for emerging zoonotic pathogens has been accelerated through the application of machine learning algorithms to screen compounds for efficacy against viruses with high epidemic potential.

Experimental Protocol 3: Machine Learning for Antiviral Discovery

• Template Selection: Use protein structures of related, well-characterized viruses as templates (e.g., measles virus as blueprint for henipaviruses) [14].

• Virtual Screening:

  • Employ machine learning algorithms (e.g., Rhodium software) to screen compound libraries
  • Rank compounds based on binding effectiveness to target viral structures
  • Filter out compounds with predicted toxicity profiles

• Validation:

  • Test top candidate compounds in high-containment laboratories (BSL-4 for maximum pathogenicity)
  • Evaluate effectiveness in inhibiting viral replication
  • Assess cytotoxicity in host cells [14]

This approach has identified 30 potentially viable viral inhibitors for Nipah and Hendra henipaviruses from an initial library of 40 million compounds, dramatically accelerating the initial discovery phase of therapeutic development [14]. The method is particularly valuable for studying highly pathogenic viruses that require stringent biosafety containment, as virtual screening can prioritize the most promising candidates before laboratory investigation.

Research Toolkit for Host Jump Studies

Table 4: Essential Research Reagents and Computational Tools

Tool/Reagent	Application	Function	Example/Reference
NCBI Virus Database	Data source	Repository of viral sequences and metadata	~12 million sequences [10]
VIRION Database	Host-virus associations	Comprehensive vertebrate-virus interaction data	Global Virome in One Network [13]
Phylogenetic Factorization	Evolutionary analysis	Identify clades with unusual epidemic potential	phylofactor R package [13]
Alignment-free Methods	Comparative genomics	Compare organisms without sequence alignment	k-word frequency analysis [15]
Rhodium Software	Drug discovery	Machine learning for compound screening	Henipavirus therapeutic identification [14]
BSL-4 Laboratories	Pathogen research	Safe study of high-containment viruses	Henipavirus validation studies [14]
Kgp-IN-1 hydrochloride	Kgp-IN-1 hydrochloride, MF:C19H25ClF4N4O3, MW:468.9 g/mol	Chemical Reagent	Bench Chemicals
Mlkl-IN-2	Mlkl-IN-2, MF:C26H25N5O, MW:423.5 g/mol	Chemical Reagent	Bench Chemicals

Discussion and Future Directions

The evolutionary correlates of viral host jumps present a complex picture of continuous pathogen exchange across species boundaries. Several key findings have emerged from recent genomic studies that reshape our understanding of viral emergence. First, the directionality of transmission is more balanced than traditionally conceptualized, with humans serving as a significant source of viruses for other animals rather than solely as recipients [10] [11] [12]. Second, viral host jumps are consistently associated with detectable genomic adaptation, though the extent of this adaptation is influenced by the pre-existing host range of the virus [10]. Third, the specific genomic targets of selection vary across viral families, suggesting multiple evolutionary pathways to successful host colonization [10].

Future research directions should focus on integrating genomic data with ecological and phenotypic variables to build predictive models of viral emergence. The expanding application of machine learning approaches, as demonstrated in therapeutic discovery for henipaviruses [14], shows promise for identifying high-risk virus-host combinations before spillover events occur. Additionally, enhanced surveillance of underrepresented host groups and geographic regions will be essential for creating a more complete picture of global viral diversity and its evolutionary dynamics [10].

The methodological approaches outlined in this review provide a framework for continued investigation into the evolutionary correlates of host jumps. As genomic sequencing technologies become more accessible and computational methods more sophisticated, our ability to detect signals of cross-species adaptation in real-time will dramatically improve. This progress may eventually enable preemptive identification of viruses with high potential for successful host jumps, transforming our approach to pandemic preparedness from reactive to proactive.

This technical review has synthesized current evidence regarding the evolutionary correlates of viral host jumps, with particular emphasis on genomic adaptation and natural selection. Several key conclusions emerge: (1) humans are both sources and sinks of viral spillover, with anthroponotic transmission occurring more frequently than traditionally appreciated; (2) host jumps consistently drive accelerated viral evolution, with the extent of adaptation inversely related to pre-existing host breadth; and (3) the genomic targets of selection during host jumps vary substantially across viral families. These insights, derived from analysis of millions of viral sequences, provide a foundation for developing more effective surveillance strategies, targeted therapeutic interventions, and predictive models for viral emergence. As the field continues to evolve, integration of genomic, ecological, and epidemiological data through multidisciplinary approaches will be essential for mitigating the threats posed by emerging viral diseases.

The escalating frequency of emerging infectious diseases represents a critical threat to global health, with the majority of these diseases originating from animal populations. Scientific consensus indicates that approximately 60-75% of emerging human infectious diseases are zoonotic, meaning they are transmitted from animals to humans [16] [9]. The process of zoonotic spillover—the transmission of pathogens from animal reservoirs to human populations—is not random but is powerfully mediated by specific ecological and environmental triggers [16]. This whitepaper examines the principal drivers of zoonotic disease emergence through a multidisciplinary lens, focusing on three interconnected phenomena: land-use change, climate change, and the nature of human-animal interfaces. Understanding these mechanisms is paramount for researchers and drug development professionals working to anticipate, prevent, and mitigate future pandemic threats.

Land Use Change as a Driver of Spillover

Mechanisms of Pathogen Emergence

Land-use change represents one of the most significant anthropogenic factors altering the dynamics of pathogen transmission between wildlife and human populations. The conversion of natural habitats for human purposes directly facilitates zoonotic spillover through several interconnected mechanisms:

• Habitat Fragmentation and Biodiversity Loss: When natural landscapes are degraded or destroyed, the resulting habitat fragmentation typically reduces overall biodiversity. However, certain species that are competent reservoirs for zoonotic pathogens (such as rodents and bats) often survive and proliferate in human-dominated landscapes, increasing the local density of infected hosts and the force of infection for pathogens they carry [17]. One study projected habitat loss for 336 terrestrial vertebrate species in the southeastern United States by 2051, finding that reptiles and species associated with open vegetation were particularly vulnerable to land-use changes [18].

• Increased Human-Animal Contact (HAC): Deforestation and agricultural expansion bring humans into closer proximity with wildlife species that may serve as pathogen reservoirs. This contact occurs through multiple exposure pathways, including occupational (e.g., logging, farming), consumptive (e.g., hunting, wildlife trade), and environmental (e.g., residing near forest edges) activities [19]. A systematic review protocol highlights that the relationship between changing HAC patterns and zoonotic disease emergence requires further empirical quantification [19].

• Ecological Disturbance and Animal Behavior Changes: Land-use change forces alterations in wildlife behavior and movement patterns. Animals displaced from their natural habitats may venture into human settlements in search of food, creating new transmission pathways for pathogens. The loss of wildlife habitats due to urban and agricultural expansion is particularly pronounced in tropical regions with high biodiversity [18].

Quantitative Impacts of Land Use Change

Table 1: Projected Impacts of Land-Use Change on Wildlife Habitats in the Southeastern United States

Land-Use Scenario	Projected Habitat Loss	Most Vulnerable Species Groups	Primary Drivers
Business-as-usual	Relatively low across Southeast, but variable by ecoregion	Reptiles, open vegetation species	Urban and crop expansion
Increased crop commodity prices	Exacerbated habitat loss in most ecoregions	Grassland-associated species	Crop expansion
Conservation policies (reduced sprawl, payments for conservation)	Reduced habitat loss in some regions	Varies by region and policy focus	Constrained urban and agricultural expansion

Source: Adapted from Martinuzzi et al. [18]

Climate Change and Vector-Borne Disease Dynamics

Climatic Influences on Pathogen Transmission

Climate change exerts multifaceted influences on the distribution and transmission dynamics of infectious diseases, particularly those spread by arthropod vectors. Key climate-sensitive mechanisms include:

• Vector Distribution and Abundance: Arthropod vectors such as mosquitoes, ticks, and sand flies are ectothermic (cold-blooded), making their survival, reproduction rates, and geographic distribution highly sensitive to climatic variables [20]. Warmer temperatures can expand the suitable habitats for disease vectors like mosquitoes that transmit dengue, chikungunya, and West Nile virus [21] [20].

• Pathogen Development and Transmission Efficiency: Temperature and humidity affect the extrinsic incubation period of pathogens within vectors—the time required for a pathogen to develop and become infectious. Warmer temperatures often accelerate pathogen replication, potentially increasing transmission efficiency [20]. One review noted that rising temperatures "can improve many characteristics of the arthropod carrier life cycle, including survival, arthropod population, pathogen communication, and the spread of infectious agents from vectors" [20].

• Seasonal Transmission and Geographic Range Expansion: Climate change has already extended the seasonal activity of ticks in temperate regions and facilitated the northward expansion of mosquito-borne diseases like dengue and chikungunya [21]. The unequal increase in average nighttime temperatures compared to daytime temperatures creates ideal conditions for vector insect growth and disease spread [20].

Major Climate-Sensitive Vector-Borne Diseases

Table 2: Climate-Sensitive Vector-Borne Diseases and Associated Vectors

Disease	Primary Vector(s)	Pathogen Type	Key Climate Influences
Lyme disease	Ticks (Ixodes genus)	Bacterium (Borrelia)	Warmer winters increasing tick survival and geographic range [20]
Dengue fever	Mosquitoes (Aedes aegypti, Ae. albopictus)	Virus	Temperature affecting mosquito reproduction, viral replication, and seasonal transmission [20]
West Nile virus disease	Mosquitoes (Culex species)	Virus	Temperature and precipitation patterns influencing mosquito abundance and bird reservoir movement [21]
Leishmaniasis	Sand flies	Parasite (Leishmania)	Temperature and humidity affecting sand fly survival and distribution [20]
Malaria	Mosquitoes (Anopheles species)	Parasite (Plasmodium)	Temperature, rainfall, and humidity affecting mosquito breeding sites and parasite development [20]

Human-Animal Interfaces and Viral Adaptation

High-Risk Interfaces for Cross-Species Transmission

Specific environments where humans and animals interact closely create ideal conditions for viral exchange and adaptation:

• Live Animal Markets and Wildlife Trade: These settings, often called "wet markets," confine diverse animal species in close proximity, facilitating pathogen transmission between species that would rarely encounter each other in nature. The mixing of species enables pathogens to reassort or recombine genetically, potentially generating novel variants with enhanced transmissibility or host range [16] [17]. The handling, poaching, and consumption of wild animal meat significantly increase spillover risk, as demonstrated by the suspected origin of HIV from non-human primate meat handling [16].

• Agricultural Systems and Intensive Farming: Modern livestock operations, particularly those with high animal densities, can act as amplification sites for zoonotic pathogens. The "panzootic" potential of pathogens like H5N1 avian influenza is exemplified by its ability to infect and spread among numerous species, including wild birds, poultry, dairy cows, and various mammals [17]. Since March 2023, 66 confirmed human H5N1 infections have been reported in the United States, primarily among farm workers [17].

• Forest-Edges and Peri-Urban Settlements: As human settlements expand into natural habitats, the interface zones between human-dominated and wild landscapes become hotspots for potential spillover events. These areas facilitate contact between wildlife, domestic animals, and humans, creating multiple opportunities for pathogen exchange [19] [22].

Molecular Mechanisms of Host Switching

Pathogens employ several strategic mechanisms to overcome species barriers and establish infections in new host populations:

• Viral Reassortment: This process occurs when a host cell is co-infected with two different influenza virus strains, allowing the viruses to exchange genetic segments and create novel progeny viruses with mixed genomes. Reassortment has played a role in at least three of the last four influenza pandemics [23].

• Genetic Mutations and Adaptive Evolution: Accumulation of point mutations in viral genomes, particularly in genes encoding surface proteins, can alter host tropism by enabling binding to different host cell receptors. RNA viruses with high mutation rates are particularly adept at this form of adaptation [16].

• Recombination: Some viruses, including coronaviruses, can undergo recombination—breaking and rejoining their genetic material—when two related viruses infect the same cell. This process can generate novel viral variants with changed host ranges or pathogenic properties [16].

Experimental Approaches and Research Methodologies

Surveillance and Pathogen Discovery Protocols

Ongoing surveillance at human-animal interfaces represents a critical frontline defense against emerging zoonotic threats. The following diagram illustrates an integrated One Health surveillance framework:

Integrated One Health Surveillance Framework for Zoonotic Pathogen Discovery

A recent study in Pakistan implemented this methodology, screening more than 1,700 swab samples from humans, poultry, and livestock collected between 2019 and 2021 [24]. This surveillance detected molecular evidence of bovine adenovirus type 2 (BAdV-2) in a 22-year-old man with respiratory symptoms—the first time this cattle virus had been detected in a human [24]. The genetic analysis showed strong similarity to strains previously isolated from cows in Spain and Japan, suggesting cross-species transmission [24].

Conjoint Analysis for Zoonotic Disease Prioritization

To address the challenge of allocating limited resources for zoonotic disease research and preparedness, researchers have employed conjoint analysis (CA), a quantitative method adapted from market research. This approach involves:

• Criteria Identification: Through focus groups using nominal group technique, researchers identified 21 measurable criteria for assessing zoonotic disease priority, including transmissibility, severity, economic impact, and preventability [9].

• Experimental Design: A partial-profile choice-based conjoint survey presents participants with 14 choice tasks, each showing five disease combinations with varying levels of 5 of the 21 criteria using an orthogonal experimental design [9].

• Preference Elicitation: Health professionals (epidemiologists, public health practitioners, physicians, veterinarians) are forced to make trade-offs between disease characteristics, revealing the implicit relative importance of each criterion through their choices [9].

• Model Fitting: Hierarchical Bayes models are fitted to the survey data to derive CA-weighted scores for disease criteria, which are then applied to rank 62 zoonotic diseases by priority [9]. This method produced better-fitted models (83.7-84.2%) compared to surveys of the general public (79.4%) [9].

The Scientist's Toolkit: Essential Research Reagents and Methods

Table 3: Key Research Reagents and Methodologies for Zoonotic Spillover Investigation

Research Tool Category	Specific Examples	Application in Spillover Research
Molecular Detection	PCR primers for conserved viral families, metagenomic sequencing kits, random amplification methods	Pathogen discovery in human and animal samples without prior knowledge of causative agent [24]
Genomic Characterization	Next-generation sequencing platforms, phylogenetic analysis software (BEAST, RAxML), sequence alignment tools	Determining evolutionary relationships between pathogens from different species and geographic locations [24]
Serological Assays	ELISA with recombinant antigens, virus neutralization tests, protein microarray platforms	Detecting previous infection and measuring cross-reactive immune responses across host species [24]
Cell Culture Systems	Primary cell cultures from multiple species, organoid models, air-liquid interface cultures	Assessing viral host range and tissue tropism in controlled laboratory conditions [16]
Animal Models	Humanized mice, ferret transmission models, non-human primates	Studying pathogenesis and transmission efficiency of emerging pathogens [16]
3-bromo-N-phenylpyridin-4-amine	3-Bromo-N-phenylpyridin-4-amine|CAS 239137-42-9	High-purity 3-Bromo-N-phenylpyridin-4-amine (CAS 239137-42-9) for pharmaceutical research and as a chemical building block. For Research Use Only. Not for human or veterinary use.
Malic Enzyme inhibitor ME1	Malic Enzyme inhibitor ME1, MF:C20H21N3O3, MW:351.4 g/mol	Chemical Reagent

The complex interplay between land-use change, climate change, and human-animal interfaces creates evolving pathways for zoonotic pathogen emergence. Deforestation and agricultural expansion drive wildlife into closer contact with human populations, while climate change extends the geographic and seasonal ranges of arthropod vectors. Simultaneously, high-risk interfaces such as live animal markets and intensive farms provide ideal conditions for viral adaptation and recombination. A proactive approach to pandemic prevention requires integrated surveillance systems that monitor pathogens across species boundaries, standardized methodologies for prioritizing zoonotic threats, and interdisciplinary collaboration under the One Health framework. For researchers and drug development professionals, understanding these ecological and environmental triggers is not merely an academic exercise but an essential component of global health security in an era of rapid environmental change.

The study of viral cross-species transmission has traditionally been dominated by a zoonotic paradigm, focusing on the jump of pathogens from animal populations into humans. This perspective has guided global surveillance efforts, public health policies, and fundamental research for decades. However, emerging genomic evidence now challenges this unidirectional view, revealing that humans frequently act as sources of viruses for other animals—a process termed anthroponotic transmission. This whitepaper synthesizes recent large-scale genomic findings that quantify the surprising frequency of anthroponotic spillover and examines its evolutionary drivers, mechanisms, and implications for pandemic preparedness. Framed within the broader context of viral zoonotic potential and species jump research, this analysis provides researchers, scientists, and drug development professionals with a updated framework for understanding the bidirectional nature of viral traffic across species boundaries.

The Genomic Evidence for Bidirectional Spillover

Recent research leveraging the entirety of publicly available viral genomic data has fundamentally altered our understanding of spillover dynamics. A comprehensive 2024 analysis of ~12 million viral sequences from NCBI Virus, employing sophisticated network and phylogenetic methods, revealed that humans are as much a source as a sink for viral spillover events [10]. Counter to conventional wisdom, this study found more documented viral host jumps from humans to other animals than from animals to humans [10].

This surprising finding emerges from analysis of 58,657 quality-controlled viral genomes spanning 32 viral families and associated with 62 vertebrate host orders. To overcome limitations of traditional viral taxonomy, researchers implemented a species-agnostic network approach that defined 5,128 "viral cliques" as discrete taxonomic units [10]. This methodology proved highly concordant with ICTV-defined species (median adjusted Rand index = 83%) while enabling more standardized comparison of host jump frequencies [10]. The analysis demonstrated that viral cliques involving only animals represent 62% of all cliques, highlighting the extensive diversity of animal viruses within the global viral-sharing network, with humans serving as a frequent bridge for cross-species transmission [10].

Table 1: Summary of Viral Genomic Data Analysis from NCBI Virus

Analysis Aspect	Findings	Implications
Total Sequences Analyzed	11,645,803 sequences (93% vertebrate-associated)	Massive dataset provides robust foundation for conclusions
Human vs. Non-Human Sequencing	93% of vertebrate sequences were human-associated	Highlights surveillance bias, yet still reveals anthroponotic dominance
Host Jump Directionality	More human-to-animal jumps than animal-to-human	Challenges fundamental assumption of unidirectional spillover risk
Viral Clique Composition	62% of cliques involved only animals	Demonstrates extensive animal viral diversity in sharing network

Genomic Surveillance Gaps and Biases

The interpretation of anthroponotic transmission frequency must be contextualized within significant surveillance gaps and metadata challenges. Current genomic surveillance displays a profound human-centric bias, with 93% of vertebrate-associated viral sequences originating from humans [10]. The next four most-sequenced viruses come from domestic animals (Sus, Gallus, Bos, and Anas), collectively representing 15% of vertebrate viral sequences after excluding SARS-CoV-2 [10]. Viruses from the remaining vertebrate genera constitute a mere 9% of sequences, indicating substantial surveillance blind spots [10].

Geographic distribution of non-human viral sequences is similarly skewed, with most samples collected from the United States and China, while regions of high biodiversity in Africa, Central Asia, South America, and Eastern Europe remain severely underrepresented [10]. Furthermore, metadata quality presents significant challenges for analysis, with 45% of non-human viral sequences lacking host information at the genus level and 37% missing sample collection dates [10]. These limitations necessitate cautious interpretation of spillover frequencies while highlighting critical needs for more balanced global surveillance.

Evolutionary Drivers and Correlates of Host Jumps

Beyond quantifying spillover direction, genomic analyses reveal fundamental evolutionary patterns associated with successful host jumps. Viral lineages involving putative host jumps demonstrate heightened evolutionary rates, suggesting significant adaptive evolution during cross-species transmission [10]. The extent of adaptation associated with host jumps appears inversely related to host range, with generalist viruses exhibiting lower levels of adaptive evolution during spillover events compared to specialist viruses [10].

The genomic targets of natural selection vary substantially across viral families. In some families, structural genes represent the prime targets of selection during host jumps, while in others, auxiliary genes show the strongest signatures of adaptation [10]. This variation reflects diverse molecular mechanisms underlying host adaptation and suggests that predictive models of spillover risk must account for viral taxonomy-specific evolutionary patterns.

Table 2: Evolutionary Correlates of Viral Host Jumps

Evolutionary Feature	Correlation with Host Jumps	Research Implications
Evolutionary Rate	Heightened in jumping lineages	Suggests strong selective pressure during spillover
Host Range	Inverse correlation with adaptation extent	Generalist viruses require less adaptation for new hosts
Genomic Targets of Selection	Varies by viral family	Points to multiple molecular pathways for host adaptation
Phylogenetic Distribution	Clustered in specific lineages	Indicates some lineages have higher jump capacity

Methodological Framework for Analyzing Spillover Events

Viral Clique Classification and Host Assignment

The identification of anthroponotic transmission events requires robust taxonomic and phylogenetic methods. The viral clique approach provides a species-agnostic classification framework that groups viruses into operational taxonomic units with similar genetic diversity, overcoming inconsistencies in ICTV species demarcation [10]. This method utilizes network theory to partition viral diversity into biologically meaningful units, achieving 95% monophyly in resulting cliques [10].

Phylogenetic Reconstruction and Host Jump Inference

For each viral clique, curated whole-genome alignments form the basis for maximum-likelihood phylogenetic reconstruction [10]. For segmented viruses, single-gene alignments are employed instead due to complications from reassortment [10]. Trees are rooted using suitable outgroups identified through alignment-free distance metrics, enabling directional inference of host jumps.

Ancestral state reconstruction of host associations then allows identification of putative cross-species transmission events. Statistical approaches differentiate true host jumps from artifacts of surveillance bias or phylogenetic uncertainty, providing robust quantification of anthroponotic versus zoonotic transmission frequencies.

Experimental Models and Research Tools

Studying anthroponotic transmission requires specialized experimental approaches and reagents that enable investigation of both human-to-animal and animal-to-human transmission pathways. The following research toolkit highlights essential resources for this emerging field.

Table 3: Research Reagent Solutions for Anthroponotic Transmission Studies

Reagent/Resource	Function	Application in Spillover Research
Viral Clique Classifier	Species-agnostic viral classification	Standardizes taxonomic units for cross-study comparison of host jumps
NCBI Virus Database	Repository of viral sequences and metadata	Primary data source for large-scale genomic analyses (11.6M+ sequences)
Whole-Genome Alignments	Phylogenetic reconstruction	Enables ancestral host state reconstruction and jump inference
BERT-infect Model	Machine learning for infectivity prediction	Predicts human infectivity potential from viral sequences [25]
Rhodium Software	Virtual compound screening	Identifies potential treatments for zoonotic pathogens [26]

Integration with Spillover Risk Prediction Models

The frequency of anthroponotic transmission has important implications for machine learning approaches to spillover risk prediction. Current models face significant challenges in predicting human infectivity potential, particularly for specific viral lineages including SARS-CoV-2 [25]. The BERT-infect model, which leverages large language models pre-trained on extensive nucleotide sequences, represents a advancement in predicting human infectivity from viral sequences [25].

However, high-resolution phylogenetic evaluation reveals general limitations in current machine learning models, including difficulty in alerting to human infectious risk in specific zoonotic viral lineages [25]. This underscores the complex relationship between sequence features and cross-species transmissibility, suggesting that anthroponotic potential may be influenced by factors beyond primary sequence composition.

One Health Implications and Future Directions

The surprising frequency of anthroponotic transmission underscores the interconnected nature of human, animal, and environmental health. Anthroponotic spillover can impede biodiversity conservation, impact food security through transmission to livestock, and potentially establish novel animal reservoirs that may reseed human populations with genetically adapted viruses [10]. This creates a complex feedback loop wherein viruses cycling between human and animal populations may acquire adaptations that increase their transmissibility or pathogenicity in humans.

Future research directions should address critical knowledge gaps, including:

• Developing integrated surveillance systems that monitor viral traffic bidirectionally
• Elucidating molecular mechanisms underlying both zoonotic and anthroponotic transmission
• Refining machine learning models to account for anthroponotic potential
• Investigating ecological and anthropogenic factors that facilitate human-to-animal transmission

Genomic evidence has fundamentally reshaped our understanding of viral traffic dynamics, revealing that anthroponotic transmission occurs more frequently than traditionally appreciated. This paradigm shift underscores the bidirectional nature of viral exchange across species boundaries and highlights the need for integrated approaches to pandemic preparedness that account for both zoonotic and anthroponotic pathways. By leveraging large-scale genomic data, novel classification systems, and evolving computational methods, researchers can better elucidate the complex factors governing viral host jumps, ultimately strengthening our ability to predict, prevent, and manage emerging infectious disease threats across species. The surprising frequency of human-to-animal transmission serves as both a cautionary note about our role in disease ecology and an opportunity to develop more comprehensive frameworks for understanding viral evolution and spread.

Within the context of viral zoonotic potential and species jump research, understanding the animal reservoirs that harbor and transmit pathogens is a cornerstone of pandemic prevention. Over 70% of emerging infectious diseases in humans are zoonoses, originating from animals, with a significant proportion originating from wildlife [27] [13] [28]. Among wildlife, bats, rodents, and birds are recognized as critical reservoirs for a diverse range of viruses with varying epidemic potential. These animal groups are not merely passive hosts; their unique physiological, immunological, and ecological traits create niches that facilitate virus maintenance, evolution, and eventual spillover into human populations [29] [30]. This whitepaper provides an in-depth technical guide to the reservoir profiles of these key animal hosts, synthesizing quantitative data, experimental approaches, and the latest research to inform researchers, scientists, and drug development professionals in the field.

Comparative Reservoir Profiles of Key Animal Hosts

The following table summarizes the comparative viral richness and zoonotic potential among bats, rodents, and birds, based on global virus databases and comparative analyses.

Table 1: Comparative Viral Richness and Zoonotic Potential of Key Animal Hosts

Host Group	Approx. Number of Species	Total Viruses Detected	Zoonotic Viruses Identified	Notable Viral Families & Pathogens
Bats (Chiroptera)	~1,400 [29]	>200 (27 families) [31]	61 [30]	Coronaviridae (SARS-CoV, MERS-CoV, SARS-CoV-2) [27] [32] [33], Paramyxoviridae (Hendra, Nipah) [27], Rhabdoviridae (Rabies) [27], Filoviridae (Ebola, Marburg) [27]
Rodents (Rodentia)	~2,200 [30]	173 (from a sample of 825 records) [31]	68 [30]	Hantaviridae (Sin Nombre, Puumala) [31] [30], Arenaviridae (Lassa) [31] [30], Flaviviridae (Tick-borne encephalitis) [31]
Birds (Aves)	~10,000 [29]	Information Missing	Information Missing	Orthomyxoviridae (Influenza A H5N1, H7N9) [29], Coronaviridae (Gammacoronavirus, Deltacoronavirus) [29], Flaviviridae (West Nile) [29]

Host Traits Influencing Zoonotic Potential

The propensity of a host species to act as an efficient reservoir and source of zoonotic viruses is influenced by a confluence of life-history, ecological, and immunological traits.

Table 2: Key Traits Associated with Zoonotic Viral Richness

Trait Category	Bats	Rodents	Birds
Life-History & Physiology	Long lifespan for body size, small litter size (e.g., one young), torpor/hibernation use [27] [30].	High variability in life-history strategies across species [30].	High metabolic rate, long lifespan for body size [29].
Ecological & Social Factors	High gregariousness (dense colonies), migratory behavior, ability to fly [27] [30].	High sympatry (overlap with other rodent species) is a key predictor [30].	High mobility due to flight, migration, congregation in large flocks [29].
Immunological Factors	Distinct immune adaptations tied to flight (e.g., enhanced DNA damage repair, dampened inflammation) enabling viral tolerance [29] [13].	Information Missing	Information Missing
Anthropogenic Interface	Peridomestic habits, roosting in human structures, hunted as bushmeat [34] [30].	Synanthropic species living in close proximity to humans [30].	Colonization of urban environments, close contact with poultry [29].

Experimental Methodologies for Reservoir Studies

Field Surveillance and Viral Detection

The foundational step in reservoir research is the field-based detection and characterization of viruses in wild hosts.

Protocol 1: Sample Collection and Broad-Spectrum RT-PCR for Coronaviruses

• Sample Collection: Rectal, fecal, oral, and nasal swab samples are collected from live-captured or peridomestic animals. Samples are immediately stored in viral transport media and frozen at -70°C until processing [35].
• RNA Extraction: Total RNA is extracted from diluted and homogenized swab samples using a standard TRIzol Reagent protocol or commercial kits [35].
• Primer Design: Due to high viral diversity, primers are often designed to target conserved regions within essential viral genes. For coronaviruses, the RNA-dependent RNA polymerase (RdRp) gene within the open reading frame 1b (ORF1b) is a common target.
  • Method: Reference sequences for the target gene are downloaded from databases (e.g., NCBI Virus). Multiple sequence alignments are performed for members of each viral genus (e.g., Alphacoronavirus, Betacoronavirus) using software like MAFFT or Clustal Omega to identify conserved regions. Primers are manually designed to these regions with annealing temperatures near 65°C [35].
• RT-PCR and Sequencing: Reverse transcription PCR (RT-PCR) is performed on the extracted RNA. Amplicons of the expected size are Sanger-sequenced. The resulting sequences are compared to public databases (e.g., GenBank) via BLAST for initial identification and then used for phylogenetic analysis [35].

Protocol 2: Metagenomic Next-Generation Sequencing (mNGS)

mNGS allows for the unbiased detection of both known and novel viruses in a sample.

• Workflow:
  • Sample Processing: Sample (e.g., swab, tissue) undergoes nuclease treatment to digest unprotected nucleic acids, enriching for viral particles.
  • Library Preparation: Nucleic acids are extracted, and sequencing libraries are constructed without target-specific amplification.
  • High-Throughput Sequencing: Libraries are sequenced on platforms like Illumina or Nanopore.
  • Bioinformatic Analysis: Host reads are filtered out. Remaining reads are assembled de novo or mapped to reference databases for viral identification and genome reconstruction [28].

Diagram 1: Viral Reservoir Study Workflow

Analyzing Cross-Species Transmission and Macroevolution

To understand the origins of zoonotic viruses and predict future spillover events, researchers employ phylogenetic and evolutionary models.

Protocol 3: Bayesian Phylogeographic and Host-Reconstruction Analysis

This methodology is used to infer the evolutionary history of viruses, including their ancestral hosts and spatial spread.

• Data Compilation: A large dataset of viral sequences with associated metadata (host species, date, and geographic location of sampling) is compiled. The sequences are aligned, and the best-fit nucleotide substitution model is selected.
• Phylogenetic Inference: A Bayesian statistical framework, implemented in software like BEAST, is used to reconstruct a time-scaled phylogenetic tree [33].
• Ancral State Reconstruction: Host family/genus or geographic location is defined as a discrete character state. The model reconstructs the most likely state for each node in the tree, tracing the evolutionary history of host switching and viral dispersal [33].
• Identification of Key Transmissions: A Bayesian Stochastic Search Variable Selection (BSSVS) procedure identifies well-supported host switches or migration events between locations over evolutionary time, calculating Bayes Factors to estimate their significance [33].

The Scientist's Toolkit: Key Research Reagents & Solutions

Table 3: Essential Reagents and Materials for Viral Reservoir Research

Reagent/Material	Function/Application	Example from Literature
Viral Transport Media (VTM)	Preserves viral pathogen integrity during transport from the field to the laboratory.	Used in sampling of wild bats and other mammals to maintain RNA viability [35] [33].
Universal / Degenerate Primers	Allows detection of a broad range of viruses or viral subtypes by targeting conserved genomic regions.	Primers targeting the RdRp gene of coronaviruses enable detection of known and novel CoVs across bat species [35].
Positive Control Plasmids & Viral Strains	Validates PCR assay performance and serves as a reference in phylogenetic analyses.	Studies use propagated vaccine strains (e.g., Avian infectious bronchitis virus) or synthetic plasmids containing viral gene fragments as controls [35].
Phylogenetic Analysis Software (BEAST)	Reconstructs evolutionary relationships, estimates divergence times, and models ancestral states and biogeographic history.	Used to identify Rhinolophus bats as the ancestral host of SARS-related coronaviruses and to pinpoint cross-species transmission events [33].
Network Analysis Algorithms	Quantifies connectivity and centrality in host-virus networks to identify key reservoir species and viruses with high spillover risk.	Applied to show bat-virus networks are more highly connected than rodent-virus networks, indicating greater potential for pathogen sharing [31].
LEI-401	LEI-401, MF:C24H31N5O2, MW:421.5 g/mol	Chemical Reagent
(R)-Sitcp	(R)-SITCP|CAS 856407-37-9|Research Chemical

Bats, rodents, and birds are pivotal players in the ecosystem of zoonotic viruses, each contributing uniquely to the landscape of emergence risk. Bats, with their high viral diversity and unique immunology, are potent reservoirs for highly virulent pathogens. Rodents, due to their high species richness and synanthropic tendencies, host a large absolute number of zoonotic viruses. Birds, through their mobility and role in agriculture, are central to the spread of viruses like avian influenza. Future research must move beyond binary assessments of zoonotic risk and integrate quantitative measures of viral epidemic potential, including virulence and transmissibility [13]. Cutting-edge research already demonstrates that high viral epidemic potential is not uniformly distributed across all bat species, but is clustered within specific clades, such as those in the families Rhinolophidae and Vespertilionidae [13] [33]. Prioritizing surveillance in these hotspots, defined by both host phylogeny and anthropogenic pressure, combined with a deeper mechanistic understanding of viral tolerance, will be crucial for preempting the next pandemic.

From Data to Prediction: Genomic Surveillance and Machine Learning for Risk Assessment

The increasing frequency of viral emergences with zoonotic origins has underscored the critical importance of large-scale genomic analyses in pandemic preparedness. Next-generation sequencing (NGS) technologies have revolutionized our ability to decrypt viral nucleotide sequences, generating tens of petabases of publicly available sequencing data that enable researchers to investigate the evolutionary drivers of cross-species transmission [36] [37]. The exponential growth of viral genomic data, including over 11 million sequences in NCBI Virus alone, provides an unprecedented resource for understanding the mechanisms underlying viral host jumps [10]. This technical guide explores how phylogenetic and network analyses of these datasets can illuminate the genetic correlates of zoonotic potential, offering researchers methodologies to identify high-risk viruses before they emerge in human populations.

The conceptual foundation for this approach rests on understanding that viruses vary in their degree of generalism and their distribution across the phylogenetic landscape of potential hosts. Viruses exhibiting phylogenetic aggregation—characterized by discrete clusters of related host species—demonstrate significantly higher zoonotic potential, likely because they have repeatedly closed phylogenetic distances to new hosts, acquired epidemiologically relevant hosts, and maintained fitness in phylogenetically aggregated host communities [38]. By harnessing large-scale genomic analyses, researchers can now move beyond reactive surveillance toward predictive frameworks that identify these patterns before widespread human transmission occurs.

Theoretical Framework: Genomic Correlates of Zoonotic Potential

Phylogenetic Patterns Predictive of Cross-Species Transmission

Large-scale comparative analyses of mammalian viruses have identified specific phylogenetic patterns associated with successful cross-species transmission. Phylogenetic aggregation emerges as a key predictor, where viruses with hosts distributed in discrete clusters across the phylogeny are more likely to be zoonotic. This aggregation metric (z.agg), calculated as the mean nearest neighbour distance across all hosts divided by the maximum phylogenetic distance, captures a virus's ability to jump and creep through host phylogenies by occasionally establishing in phylogenetically novel host species and subsequently acquiring related hosts [38].

Statistical modeling consistently identifies phylogenetic aggregation as a superior predictor of zoonotic status compared to other phylogenetic distance metrics. In model comparisons using Bayesian information criterion (BIC), aggregation was the only predictor variable other than research effort retained in the top two statistical models [38]. The negative association between aggregation values and zoonotic status indicates that zoonotic viruses demonstrate more clustered host distributions across the phylogenetic tree, reflecting their ability to overcome phylogenetic barriers through adaptive evolution.

Table 1: Phylogenetic Metrics Predicting Viral Zoonotic Status

Metric	Calculation	Interpretation	Association with Zoonotic Risk
Phylogenetic Aggregation (z.agg)	Mean nearest neighbor distance / Maximum phylogenetic distance	Measures clustering of host species in phylogeny	Negative association (lower values = higher risk)
Mean Pairwise Distance (z.mpd)	Average phylogenetic distance between all host pairs	Measures overall host range breadth	Inconsistent predictor across models
Maximum Distance (z.max)	Greatest phylogenetic distance between any two hosts	Measures ultimate span in host phylogeny	Positive in some models but inconsistent
Mean Distance to Humans (avDist)	Average phylogenetic distance from host species to humans	Measures proximity to humans in phylogeny	Negative association but not always significant

Evolutionary Signatures of Host Jump Events

Genomic analyses reveal that viral lineages involving putative host jumps demonstrate heightened evolutionary rates and require detectable adaptation to new host environments. The extent of adaptation associated with a host jump is notably lower for viruses with broader host ranges, suggesting that generalist viruses pre-adapted to multiple hosts require fewer genomic changes to infect new species [10]. This finding has profound implications for risk assessment, as it indicates that viruses with existing broad host ranges may represent higher emergence risks than specialist viruses.

Comprehensive analyses of viral host jumps across vertebrates have yielded the surprising finding that humans are as much a source as a sink for viral spillover events, with more viral host jumps inferred from humans to other animals than from animals to humans [10]. This bidirectional transmission highlights the complex network of viral exchange in which zoonotic events occur and emphasizes the importance of anthroponotic transmission in reservoir establishment that may subsequently reseed human populations.

The genomic targets of natural selection associated with host jumps vary across different viral families, with either structural or auxiliary genes being the prime targets of selection depending on the viral family [10]. This taxonomic specificity in adaptation mechanisms necessitates tailored approaches when assessing different viral taxa for zoonotic potential.

Methodological Approaches: Analytical Frameworks for Viral Genomics

Data Acquisition and Curation Protocols

The foundation of robust large-scale genomic analysis begins with comprehensive data acquisition. Researchers should aggregate viral genomic data from multiple public repositories including:

• NCBI Virus: Contains over 11 million viral sequences, though with significant biases toward human-associated viruses (93% of vertebrate-associated sequences) [10]
• GISAID: Essential for accessing SARS-CoV-2 genomic data and associated metadata
• Specialized databases: VIRION and CLOVER provide curated host-virus association data [10]

Metadata quality control represents a critical challenge, with 45% of non-human viral sequences lacking host information at the genus level and 37% missing sample collection dates [10]. Implementation of automated validation pipelines using structured vocabularies and cross-referencing with taxonomic databases can partially mitigate these issues. For phylogenetic analyses, exclusion of viruses known to infect only a single host species is necessary to calculate meaningful phylogenetic metrics [38].

Table 2: Essential Computational Tools for Viral Genomic Analysis

Tool Category	Specific Tools	Primary Function	Application in Zoonosis Research
Sequence Alignment	Nextalign, MAFFT, Clustal Omega	Multiple sequence alignment	Prepare homologous regions for phylogenetic analysis
Phylogenetic Reconstruction	IQ-TREE, RAxML, BEAST	Infer evolutionary relationships	Estimate host-virus co-evolution and divergence times
Phylogeographic Analysis	BEAST, Bayesian TraitR	Reconstruct spatial movement	Track cross-species transmission pathways
Network Analysis	Cytoscape, NetworkX	Visualize and analyze host-virus networks	Identify connectivity and centralization in transmission networks
Read Mapping	Minimap2, Genome-on-Diet	Map sequences to references	Rapid comparison of viral variants across hosts
Variant Calling	DeepVariant, LoFreq	Identify genetic variations	Detect adaptive mutations associated with host switching

Phylogenetic and Network Analysis Methodologies

Viral Clique Definition for Species-Agnostic Analysis

Traditional reliance on physical and biological properties to define viral taxa presents challenges for large-scale comparative analyses. A species-agnostic approach based on network theory defines "viral cliques" as discrete taxonomic units with similar genetic diversity, effectively partitioning genomic diversity into biologically relevant operational taxonomic units [10]. This method demonstrates high concordance with ICTV-defined species (median adjusted Rand index = 83%) while enabling consistent analysis across diverse viral families.

Implementation involves:

• Calculating pairwise genetic distances across comprehensive sequence datasets
• Applying network clustering algorithms to identify discrete units
• Validating monophyly through phylogenetic reconstruction (95% of cliques should be monophyletic)

Host Jump Identification Protocol

Putative host jumps within viral cliques are identified through a multi-step process:

• Sequence curation: Produce curated whole-genome alignments, using single-gene alignments for segmented viruses due to high reassortment frequency
• Phylogenetic reconstruction: Apply maximum-likelihood phylogenetic methods to aligned sequences
• Tree rooting: Identify suitable outgroups using metrics of alignment-free distances
• Host mapping: Reconstruct host associations for each node using parsimony or probabilistic methods
• Jump identification: Identify phylogenetic nodes where ancestral host state differs from descendant state

For temporal analysis, calibrate evolutionary rates using tip-dating approaches with known sampling dates, though datasets with short timeframes may demonstrate weak temporal signals requiring fixed clock rates [39].

Phylogeographic Reconstruction Framework

Understanding spatial patterns of emergence requires discrete phylogeographic analysis:

• Subsampling strategy: Address surveillance bias by subsampling in temporal windows proportional to regional population
• Tree reconstruction: Use efficient tree likelihood functions (e.g., BEAST) for large genomic datasets
• Location modeling: Apply asymmetric continuous-time Markov chains to estimate transition rates between locations
• Introduction events: Define as nodes where location differs from parent, counting only the oldest root when multiple subtrees coincide

This approach successfully identified that Omicron BA.5 introductions to the United States originated primarily from Europe rather than the variant's putative African origin, matching air travel patterns [39].

Computational Infrastructure for Large-Scale Genomic Analyses

Addressing Computational Scalability Challenges

The volume of genomic data generated by modern sequencing technologies often exceeds terabytes per project, creating significant computational challenges. Sparsified genomics approaches systematically exclude redundant bases from genomic sequences, enabling faster processing while maintaining analytical accuracy [37]. The Genome-on-Diet framework implements this concept through:

• Pattern-based sparsification: Using repeating pattern sequences to determine which bases to exclude
• Parallel processing: Highly parallel, memory-frugal implementation
• Accuracy preservation: Maintaining comparable accuracy to non-sparsified processing

This approach accelerates read mapping by 2.57-6.28× and reduces index sizes by 2× while providing more correctly detected variations compared to conventional methods [37].

Cloud computing platforms (AWS, Google Cloud Genomics) provide essential infrastructure for scalable genomic analysis, offering:

• Elastic scaling to handle terabyte-scale datasets
• Global collaboration capabilities through shared data environments
• Compliance with regulatory frameworks (HIPAA, GDPR) for sensitive data
• Cost-effectiveness for research groups without local computational infrastructure [36]

Workflow Integration and Automation

Implementation of robust genomic analysis pipelines requires workflow management systems that integrate discrete analytical steps while ensuring reproducibility. Common workflow language (CWL) or Nextflow pipelines should incorporate:

• Quality control and preprocessing: FastQC, Trimmomatic, or custom filters
• Variant identification and annotation: LoFreq, DeepVariant, SnpEff
• Phylogenetic reconstruction: IQ-TREE, RAxML, or BEAST with model testing
• Network analysis and visualization: Cytoscape, NetworkX, or custom scripts

Automated pipeline execution enables rapid analysis of emerging viral threats, with some systems providing early warning of variants 147 days before population-level dominance [40].

Figure 1: Comprehensive Workflow for Viral Genomic Analysis to Predict Zoonotic Risk

Case Studies in Genomic Surveillance and Prediction

SARS-CoV-2 Variant Surveillance System

The eVarEPS (Environmental SARS-CoV-2 Variations Evaluation and Prewarning System) demonstrates the power of genomic surveillance for early detection of emerging variants. This system identified amino acid mutations in 27,762 sewage sequencing datasets an average of 147 days earlier than clinical detection, with 46.62% of these variants subsequently spreading in human populations [40].

Key implementation aspects:

• Multi-dimensional risk assessment: Evaluating variants based on receptor affinity, neutralizing antibody affinity, and difficulty of amino acid change
• High-risk variant identification: 1,345 high-risk variants specifically detected in sewage before clinical recognition
• Validation: Detection of characteristic variants one month to one year earlier than corresponding lineage discovery

This approach demonstrates that tracking viral variants in environmental samples provides more sensitive and timely insights into population prevalence than clinical surveillance alone.

Cross-Species Transmission Network Analysis

A comprehensive analysis of 58,657 viral genomes across 32 viral families revealed unexpected patterns in cross-species transmission. Contrary to conventional focus on zoonotic transmission, researchers found more viral host jumps from humans to other animals (anthroponosis) than from animals to humans [10]. This finding fundamentally alters our understanding of viral emergence networks and emphasizes the importance of bidirectional transmission surveillance.

Methodological innovations enabling this discovery included:

• Host jumping quantification: Using phylogenetic reconciliation to identify cross-species transmission nodes
• Directionality assessment: Applying parsimony principles to determine transmission direction
• Network visualization: Mapping complex transmission pathways across host taxa

Experimental Protocols for Genomic Analysis of Zoonotic Potential

Protocol 1: Phylogenetic Aggregation Analysis

Objective: Quantify phylogenetic aggregation of virus host ranges to assess zoonotic potential.

Materials:

• Host-virus association database (VIRION, CLOVER, or custom)
• Dated phylogenetic tree of host species
• Computational environment (R, Python, or specialized software)

Procedure:

• Data preparation: Filter viruses to those infecting at least two host species
• Phylogenetic calculation:
  • Compute mean pairwise phylogenetic distance (mpd) between all host pairs
  • Calculate maximum phylogenetic distance (maxd) between any two hosts
  • Determine mean nearest taxon distance (mntd) across hosts
• Aggregation metric: Compute z.agg = mntd / maxd
• Standardization: Generate standard effect sizes via phylogenetic randomization
• Validation: Compare with known zoonotic status databases

Interpretation: Viruses with significantly low z.agg values demonstrate phylogenetic aggregation and higher predicted zoonotic potential [38].

Protocol 2: Host Jump Identification in Viral Clades

Objective: Identify and characterize historical host jump events within viral evolutionary history.

Materials:

• Whole-genome sequences with comprehensive host metadata
• Computational phylogenetics pipeline (IQ-TREE, BEAST)
• Statistical computing environment for discrete trait analysis

Procedure:

• Sequence alignment: Generate whole-genome alignments using codon-aware methods
• Phylogenetic reconstruction: Infer maximum-likelihood trees with bootstrap support
• Host trait mapping: Reconstruct historical host associations using maximum parsimony or probabilistic methods
• Jump identification: Identify tree nodes where descendant host differs from ancestral host
• Selective pressure analysis: Test for elevated dN/dS ratios in branches associated with host jumps
• Functional analysis: Identify specific mutations associated with host transitions

Interpretation: Branches with significantly elevated evolutionary rates and specific adaptive mutations indicate successful host jump events [10].

Figure 2: Process of Viral Host Jump Requiring Adaptive Mutations

Research Reagent Solutions for Viral Genomics

Table 3: Essential Research Reagents and Computational Tools for Viral Genomics

Category	Specific Resource	Function/Application	Implementation Notes
Sequencing Technologies	Illumina NovaSeq X, Oxford Nanopore	High-throughput viral genome sequencing	Nanopore enables real-time, portable sequencing
Variant Calling	DeepVariant, LoFreq	Identification of genetic variations	DeepVariant uses deep learning for improved accuracy
Genome Alignment	Minimap2, MAFFT	Sequence comparison and alignment	Minimap2 optimized for long-read technologies
Phylogenetic Software	IQ-TREE, BEAST, RAxML	Evolutionary relationship inference	BEAST enables relaxed molecular clock dating
Network Analysis	Cytoscape, NetworkX	Visualization of host-virus networks	Python-based NetworkX for custom analyses
Data Sparsification	Genome-on-Diet	Accelerated processing of large datasets	Systematic base exclusion maintaining accuracy
Environmental Surveillance	eVarEPS	Early warning system for variants	Sewage-based variant detection platform

The integration of large-scale phylogenetic and network analyses provides powerful tools for understanding and predicting viral emergence. The field has moved beyond descriptive studies to identify specific genomic and ecological correlates of zoonotic risk, particularly phylogenetic aggregation and pre-adaptation breadth. These approaches enable a shift from reactive to proactive surveillance, with environmental genomic monitoring providing early warnings of emerging threats months before clinical recognition.

Future directions will require enhanced computational efficiency through sparsified genomics approaches, improved metadata standards to address current gaps in host and temporal information, and integrated One Health frameworks that simultaneously monitor human, animal, and environmental viromes. By implementing the methodologies outlined in this technical guide, researchers can contribute to a global early warning system capable of identifying high-risk viruses before they emerge in human populations, potentially preventing future pandemics through timely intervention.

The majority of emerging and re-emerging infectious diseases in humans are caused by viruses that have jumped from animal populations into humans, a process known as zoonosis [10]. These zoonotic viruses have caused countless disease outbreaks ranging from isolated cases to pandemics, exacting a major toll on human health and global economies throughout history. There is a pressing need to develop better approaches to pre-empt the emergence of viral infectious diseases and mitigate their effects, which has driven immense interest in understanding the correlates and mechanisms of zoonotic host jumps [10]. While traditional surveillance efforts have typically been reactive—triggered after pathogens have already infected humans—recent advances in machine learning (ML) and artificial intelligence (AI) offer the potential for predictive capabilities that could identify high-risk viruses before they emerge in human populations [41]. This technical guide explores the current landscape of ML models designed to assess zoonotic risk, spillover potential, and epidemic preparedness, providing researchers and drug development professionals with methodologies, benchmarks, and practical implementation frameworks.

Current Landscape of Machine Learning in Zoonotic Risk Prediction

The Data Challenge and Surveillance Gaps

A significant challenge in predicting viral disease emergence stems from the characterization of only a small fraction of the viral diversity circulating in wild and domestic vertebrates. Current viral genomic data exhibits substantial biases that directly impact model performance and generalizability [10]:

• Human-centric surveillance: 93% of vertebrate-associated viral sequences in NCBI databases are human-associated, with domestic animals (Sus, Gallus, Bos, Anas) representing the next most sequenced viruses [10].
• Geographical biases: Most samples are collected from the United States and China, while countries in Africa, Central Asia, South America, and Eastern Europe are highly underrepresented [10].
• Metadata deficiencies: Approximately 45% of non-human viral sequences lack host information at the genus level, and 37% lack sample collection year data [10].

These surveillance gaps create inherent limitations in model training and validation, necessitating specialized approaches to address data scarcity and bias.

Key Machine Learning Approaches

The field has employed diverse ML techniques, each with distinct advantages for various aspects of zoonotic risk prediction:

Table 1: Machine Learning Algorithms for Zoonotic Risk Assessment

Algorithm Category	Specific Models	Key Applications	Advantages
Traditional ML	Logistic Regression, Random Forest, SVM, K-NN	Host range prediction, risk factor identification [42] [43]	Interpretability, efficiency with structured data [42]
Deep Learning	CNN, RNN/LSTM, Transformer architectures	Sequence-based risk prediction, feature extraction [43]	Handles raw sequence data, identifies complex patterns [43]
Ensemble Methods	Random Forest, XGBoost, CatBoost	Integrating diverse feature sets, ordinal regression tasks [43]	Robust performance, handles mixed data types [43]
Large Language Models	DNABERT, ViBE, BERT-infect	Viral infectivity prediction from genetic sequences [44] [25]	Transfer learning, context-aware sequence analysis [25]

Benchmarking Model Performance Across Viral Families

The BERT-infect Model and Comparative Analysis

Recent research has addressed fundamental limitations in zoonotic risk prediction through expansive dataset construction and novel model architectures. The BERT-infect model, which leverages large language models pre-trained on extensive nucleotide sequences, represents a significant advancement [44] [25]. This approach has demonstrated:

• Substantial performance boosts across 26 viral families, particularly for segmented RNA viruses involved in severe zoonoses but previously overlooked due to limited data [25].
• High predictive performance with partial sequences, including high-throughput sequencing reads or contig sequences from de novo assemblies, enabling mining of zoonotic viruses from metagenomic data [44].
• Robust predictive capability for novel viruses, with models trained on data up to 2018 demonstrating strong performance for most viruses identified post-2018 [25].

However, high-resolution phylogenetic analysis has revealed persistent limitations, particularly difficulty in alerting human infectious risk in specific zoonotic viral lineages, including SARS-CoV-2 [44] [25].

Table 2: Performance Comparison of Zoonotic Risk Prediction Models

Model Architecture	Viral Families Covered	Key Strengths	Identified Limitations
BERT-infect	26 families [25]	State-of-the-art on segmented RNA viruses; works with partial sequences [25]	Struggles with specific lineages (e.g., SARS-CoV-2) [44]
Random Forest	Influenza A viruses [43]	High performance in residue-level risk assessment; interpretable [43]	Limited to features engineered from sequences
Zoonotic Rank Model	Multiple families [25]	Incorporates genomic features and similarity to human genes [25]	Performance varies across viral families
DeePac_vir	Multiple families [25]	Uses subsequence analysis for predictions [25]	Requires substantial computational resources

Avian Influenza Virus Risk Assessment

The PB2 segment of influenza A viruses has emerged as a critical target for zoonotic risk assessment, with specialized ML frameworks developed specifically for this application [43]. Research has demonstrated:

• Random Forest superiority: For PB2 segment analysis, Random Forest regression models outperformed other approaches, including deep learning architectures, in distinguishing among risk groups [43].
• Key adaptive residues: SHAP-based risk assessment identified critical residues (271A, 627K, 591R, 588A, 292I, 684S, 684A, 81M, 199S, and 368Q) and mutations (T271A, Q368R/K, E627K, Q591R, A588T/I/V, and I292V/T) essential for zoonotic risk [43].
• Host-specific patterns: Influenza A viruses from Phasianidae showed elevated zoonotic risk scores compared to other avian species, with specific mutations (I292V/T, Q368R, A588T/I, V598A/I/T, and E/V627K) particularly significant in this family [43].

Experimental Protocols and Methodologies

Dataset Curation and Preparation

Comprehensive dataset construction is foundational to effective model development. The following protocol outlines standardized approaches for viral sequence curation:

Protocol 1: Viral Sequence Dataset Curation

• Sequence Collection: Retrieve viral sequences and metadata from NCBI Virus Database using targeted queries for viral families of interest [25].
• Host-based Labeling: Assign infectivity labels according to host information, excluding sequences from environmental samples where host organisms are ambiguous [25].
• Segmented Virus Processing: For segmented RNA viruses, group sequences into viral isolates based on metadata combinations. Eliminate redundancy by randomly sampling sequences for each segment when isolates contain excess sequences [25].
• Quality Control: Verify segment assignments in metadata to ensure multiple segment sequences are not incorrectly assigned to single viruses. Remove problematic isolates (e.g., 76 virus isolates and 1270 sequences removed in referenced study) [25].
• Temporal Splitting: Divide data into past (training) and future (validation) datasets based on sequence collection date (e.g., pre- and post-December 31, 2017) to evaluate predictive capability for novel viruses [25].

BERT-infect Model Implementation

The BERT-infect framework represents a state-of-the-art approach for viral infectivity prediction, leveraging transfer learning from pre-trained genetic language models:

Protocol 2: BERT-infect Model Development

• Model Selection: Choose pre-trained genetic language models (DNABERT pre-trained on human whole genome or ViBE pre-trained on NCBI RefSeq viral genomes) [25].
• Input Preparation: Split viral genomes into 250 bp fragments with 125 bp window size and apply 4-mer tokenization compatible with pre-trained models [25].
• Task-Specific Fine-tuning: Fine-tune BERT models using past virus datasets separately for each viral family, maintaining family-specific parameter optimization [25].
• Prediction Aggregation: For full-genome predictions, average prediction scores across all subsequences derived from a complete viral genome [25].
• Validation Framework: Implement stratified five-fold cross-validation adjusted for class imbalance of infectivity and virus genus classifications, with training/evaluation/test splits of 60%/20%/20% respectively [25].

SHAP-Based Risk Assessment for Influenza PB2

For residue-level risk interpretation, SHAP-based approaches provide explainable AI capabilities critical for scientific validation and hypothesis generation:

Protocol 3: SHAP-Based Zoonotic Risk Assessment

• Risk Group Definition: Categorize PB2 protein sequences into three ordinal risk groups: low-risk (avian influenza viruses), mid-risk (human cases of avian influenza), and high-risk (human influenza viruses) [43].
• Model Training: Implement Random Forest regression on amino acid sequence features, optimizing for ordinal separation of risk groups [43].
• SHAP Value Calculation: Apply Tree SHAP algorithm to estimate feature attributions for each residue position in the PB2 protein sequence [43].
• Residue Ranking: Aggregate SHAP values across samples to rank residues by their contribution to zoonotic risk assessment [43].
• Mutation Impact Quantification: Calculate risk yield from specific mutations by comparing SHAP value differences between reference and mutant sequences [43].

Table 3: Essential Research Reagents and Computational Resources

Resource Category	Specific Tools/Databases	Primary Function	Application in Zoonotic Risk Assessment
Sequence Databases	NCBI Virus, VIRION, CLOVER [10] [44]	Viral sequence and host association data	Training data source; host-pathogen association mapping [10]
Pre-trained Models	DNABERT, ViBE [25]	Genomic sequence representation	Transfer learning foundation for BERT-infect models [25]
Explainable AI Libraries	SHAP (Tree SHAP) [43]	Model interpretation and feature importance	Identifying key residues and mutations in viral proteins [43]
Phylogenetic Tools	Maximum likelihood reconstruction, alignment-free distance metrics [10]	Evolutionary relationship inference	High-resolution evaluation of model performance across lineages [10]
Meta-genomic Processing	EMBOSS getorf, de novo assemblers [25]	ORF annotation and sequence assembly	Preparing partial sequences from surveillance data [25]

Critical Challenges and Future Directions

Despite significant advances, current ML approaches face substantial challenges in reliable zoonotic risk prediction:

Limitations in Current Modeling Paradigms

• Lineage-Specific Blind Spots: Even state-of-the-art models demonstrate difficulty alerting human infectious risk in specific zoonotic viral lineages, including SARS-CoV-2, indicating fundamental gaps in our understanding of the genetic determinants of infectivity [44].
• Taxonomic Inconsistencies: Only 37% of user-submitted species identifiers in viral genomic databases match ICTV taxonomy, complicating robust comparative analyses across diverse viral taxa [10].
• Directionality of Host Jumps: Surprisingly, humans appear to be as much a source as a sink for viral spillover events, with more viral host jumps inferred from humans to other animals than from animals to humans [10]. This complex transmission network necessitates more sophisticated modeling approaches.

Emerging Opportunities

• Expanded Dataset Curation: Constructing datasets across diverse viral families, as demonstrated with the 26-family benchmark, substantially boosts model performance and identifies previously overlooked virus types [25].
• Integration of Evolutionary Dynamics: Viral lineages involving putative host jumps demonstrate heightened evolution, with the extent of adaptation lower for viruses with broader host ranges [10].
• Protein-Specific Adaptation Patterns: The genomic targets of natural selection associated with host jumps vary across different viral families, with either structural or auxiliary genes being prime targets of selection [10].

Machine learning models for predicting zoonotic risk represent a rapidly advancing frontier with significant potential to transform pandemic preparedness. Current approaches, particularly those leveraging large language models pre-trained on extensive genetic databases, have demonstrated substantial improvements in identifying potential spillover threats across diverse viral families. However, persistent challenges—including lineage-specific prediction failures, data biases in genomic surveillance, and incomplete understanding of the genetic determinants of host adaptation—highlight the need for continued refinement of these tools. The integration of explainable AI methodologies, such as SHAP-based residue analysis, provides critical pathways for transforming black-box predictions into testable biological hypotheses. As these technologies mature, they offer the promise of shifting viral surveillance from reactive documentation of emerging threats to proactive identification of high-risk viruses before they initiate outbreaks in human populations. For researchers and drug development professionals, these tools increasingly provide actionable insights for prioritizing virological characterization, guiding experimental studies of host adaptation, and focusing surveillance efforts on the viral lineages of greatest concern.

Spatial epidemiology investigates the patterns and determinants of health outcomes over both space and time, providing crucial insights into disease distribution and risk factors [45]. Within this field, spatio-temporal models have gained significant importance due to their capacity to incorporate spatial and temporal dependencies, uncertainties, and intricate interactions that characterize infectious disease dynamics. The emergence of Big Data in public health, characterized by volume, velocity, and variety, has further accelerated the development of sophisticated analytical approaches that can harness complex datasets ranging from genomic sequences to environmental covariates [46]. The integration of viral molecular data with traditional epidemiological information represents a particularly promising frontier for understanding and predicting pathogen spread.

The One Health approach, which acknowledges the interdependent nature of human, animal, and environmental health, provides an essential framework for modeling zoonotic diseases [47]. Approximately 60% of emerging infectious diseases are caused by zoonotic pathogens originating in animals, with about 70% of these originating in wildlife [48]. This interconnection demands analytical frameworks that can simultaneously capture transmission dynamics across multiple species and environments while incorporating the molecular characteristics that determine viral fitness and host adaptation.

Core Methodological Approaches

Statistical and Bayesian Frameworks

Bayesian spatiotemporal models have emerged as powerful tools for unraveling the intricacies of disease spread by seamlessly integrating both spatial and temporal dimensions [45]. These approaches offer several distinct advantages for epidemiological modeling:

• Uncertainty Quantification: Bayesian methods explicitly quantify uncertainty in predictions or inferential estimates using probabilities, which is crucial for public health decision-making where perfect information is rarely available.
• Information Integration: Bayesian statistics incorporates prior information alongside current sample data and overall information, allowing for the integration of historical knowledge or expert opinion into the modeling process.
• Flexibility: Bayesian approaches effectively address common modeling challenges in spatial epidemiology, including non-normality, limited sample sizes, missing data, and clustered data structures.

The foundational principle of Bayesian statistics is the derivation of posterior distributions, which are produced by integrating the current sample (using the likelihood function) with the prior distribution of parameters based on historical or external information [45]. This approach enables researchers to identify regions or times of heightened risk and uncover disease patterns that persist or evolve predictably over time and across diverse spatial units.

Key tasks accomplished by Bayesian spatiotemporal models include: assessing expected values and uncertainty of outcome variables at specific spatial points throughout observation periods; forecasting expected values at specific locations; identifying evolving disease patterns; and analyzing the influence of environmental factors on spatiotemporal disease dynamics [45].

Machine Learning Approaches

Machine learning (ML) methods represent a complementary approach to traditional statistical modeling, offering distinct advantages for handling complex, high-dimensional datasets. Two ML methods extensively used in infectious disease epidemiology are Boosted Regression Trees (BRT) and Random Forest (RF) [49].

• Boosted Regression Trees (BRT): Also known as Gradient Boosting Trees, BRT builds a large number of small decision trees, with each new tree fitted to explain the remaining variance from previous trees. This method is considered robust for spatiotemporal analyses but has a tendency to overfit unless substantial data are available.
• Random Forest (RF): RF consists of a large collection of decision trees that grow by randomly selecting inputs and predictors at each node. This randomness helps reduce overfitting and makes RF particularly robust for complex modeling tasks.

These ML approaches make no assumptions about the statistical distribution of the data and can handle non-linear and non-parametric relationships that challenge traditional statistical methods [49]. They have been successfully applied to study spatial spread of numerous infectious diseases, including epidemics among swine farms, Ebola case-fatality ratio, risk factors for visceral leishmaniasis, African swine fever, scrub typhus, and dengue incidence.

Hybrid and Advanced Approaches

Physics-Informed Neural Networks (PINNs) represent an emerging hybrid approach that incorporates observed data with mathematical physics models described by partial differential equations (PDEs) [50]. PINNs are deep learning algorithms that provide meaningful predictions even for small training datasets as they follow dynamics imposed by PDEs. This makes them particularly efficient for inverse problems and noisy data, with strong generalization properties ensured through physically consistent predictions.

Compared to classical numerical methods for inverse problems in PDEs, PINNs offer several advantages: they do not require mesh generation, are easier to implement using open-source deep learning frameworks, and have demonstrated success in diverse scientific applications including flow mechanics, heat transfer problems, electrophysiology, and wildfire front prediction [50].

Table 1: Comparison of Core Modeling Approaches in Spatio-Temporal Epidemiology

Approach	Key Features	Strengths	Ideal Use Cases
Bayesian Spatiotemporal Models	Incorporates prior knowledge, quantifies uncertainty, handles dependencies	Explicit uncertainty quantification, integrates multiple information sources	Disease mapping, risk factor identification, small area estimation
Machine Learning (BRT/RF)	Data-driven, non-parametric, handles complex interactions	Handles high-dimensional data, captures non-linear relationships	Pattern recognition, prediction with complex covariates, variable importance
Physics-Informed Neural Networks	Combines data with mechanistic models, uses PDE constraints	Works with limited data, physically consistent predictions	Systems with known mechanistic relationships, inverse problems
Linear Models	Parametric, interpretable, model averaging possible	Clear inference, uncertainty propagation, handles sparse data	Explanatory modeling, settings requiring interpretable parameters

Integration with Viral Molecular Data

Phylogenetic and Evolutionary Components

The integration of viral molecular data with spatio-temporal models significantly enhances our ability to characterize host-virus associations and predict emergence patterns. Recent research has demonstrated that viral epidemic potential is not uniformly distributed across host phylogenies, with specific clades of bats showing stronger associations with highly virulent human viruses [13].

Phylogenetic factorization approaches can iteratively partition phylogenies to identify nodes with maximum contrast in viral epidemic potential, thereby identifying particular clades with greater or lesser propensities to harbor virulent, transmissible, or high-death-burden viruses without needing to specify a given phylogenetic scale a priori [13]. This flexible graph-partitioning algorithm has revealed that virulence, transmissibility, and death burden cluster within specific bat clades, often composed largely of cosmopolitan families.

Quantifying phylogenetic signal in viral traits using metrics such as Pagel's λ and Blomberg's K provides crucial information about how conserved these traits are across evolutionary history. Values of λ near zero indicate complete phylogenetic randomness, while values near one support high phylogenetic signal (i.e., Brownian motion) [13]. Understanding this phylogenetic distribution enables more targeted surveillance and risk mitigation strategies.

Molecular Determinants of Spillover Risk

The incorporation of viral genomic data into spatio-temporal models requires careful consideration of which molecular features most strongly predict cross-species transmission and establishment in new hosts. Key factors include:

• Evolutionary Distance: As the phylogenetic distance between a vertebrate host and humans increases, a virus is less likely to be pre-adapted to overcome human host defense mechanisms, but humans are also less likely to be pre-adapted to cope with these novel infections, potentially increasing morbidity and mortality risk [13].
• Functional Traits: Specific viral adaptations, such as receptor binding affinity, polymerase fidelity, and immune evasion capabilities, can be integrated as covariates in spatio-temporal models to refine risk predictions.
• Evolutionary Rates: Measurements of evolutionary change across viral lineages, particularly in key antigenic regions, can provide early warning signals of adaptation to new hosts.

Table 2: Molecular Data Types and Their Applications in Spatio-Temporal Risk Models

Data Type	Description	Epidemiological Application	Analytical Considerations
Whole Genome Sequences	Complete genetic material of viral pathogens	Tracking transmission pathways, identifying mutations, molecular clock dating	Requires specialized phylogenetic methods, computationally intensive
Phylogenetic Trees	Evolutionary relationships among viral sequences	Understanding spread patterns, identifying transmission clusters	Tree uncertainty should be incorporated in model estimates
Antigenic Characterization	Measurement of immune recognition properties	Predicting vaccine effectiveness, understanding reinfection risk	Often requires specialized laboratory assays
Host Transcriptomics	Gene expression profiles in infected hosts	Understanding pathogenic mechanisms, identifying biomarkers	High-dimensional data requiring dimension reduction techniques
Viral Load Data	Quantification of viral concentration in samples	Inferring infectiousness, modeling transmission probability	Often has high measurement variability

Experimental Protocols and Methodologies

Force-of-Infection (FoI) Estimation

The Force-of-Infection (FoI), defined as the rate at which susceptible individuals become infected, is a crucial metric for understanding changes in disease incidence across space and time [49]. Unlike prevalence measures that integrate infection over long periods, FoI provides information about current transmission dynamics and the impact of control interventions.

Protocol: Catalytic Model Fitting for FoI Estimation

Purpose: To estimate yearly-varying FoI values from age-stratified serological data.

Materials and Reagents:

• Serum samples from representative population sampling
• Laboratory equipment for serological testing (ELISA, Western Blot, or other antigen-specific assays)
• Demographic data for study population
• Statistical computing environment (R, Python, or Stan)

Procedure:

• Data Collection: Conduct cross-sectional serosurveys with age-stratified sampling across multiple locations and time points.
• Laboratory Analysis: Test serum samples for pathogen-specific antibodies using validated serological assays.
• Data Preparation: Structure data as age-seroprevalence profiles for each serosurvey location and time point.
• Model Specification: Fit catalytic models to age-stratified seropositivity data using Bayesian methods. The basic catalytic model formula is: [ p(a) = 1 - e^{-\lambda a} ] where ( p(a) ) is the probability of being seropositive by age ( a ), and ( \lambda ) is the constant FoI.
• Time-Varying Extension: For time-varying FoI, extend the model to: [ p(a,t) = 1 - \exp\left[-\int_{t-a}^{t} \lambda(s) ds\right] ] where ( \lambda(s) ) represents the FoI at time ( s ).
• Parameter Estimation: Use Markov Chain Monte Carlo (MCMC) methods to obtain posterior distributions of FoI estimates.
• Validation: Compare model predictions with held-out data and conduct sensitivity analyses.

Applications: This approach has been successfully applied to Chagas disease [49], dengue fever [13], and other infectious diseases where serological data are available for surveillance.

Spatio-Temporal Risk Prediction Using Machine Learning

Purpose: To develop accurate spatio-temporal risk predictions by integrating heterogeneous data sources including environmental, meteorological, and socio-demographic variables.

Materials:

• Disease incidence data with precise geolocation and time stamps
• Covariate data from remote sensing, weather stations, and census records
• Geographic Information System (GIS) software
• Machine learning libraries (scikit-learn, xgboost, tidymodels)

Procedure:

• Data Preparation:
  • Aggregate disease case data to appropriate spatial and temporal resolutions
  • Extract and align covariate data for the same spatio-temporal units
  • Address missing data through imputation or exclusion
  • Split data into training, validation, and test sets with temporal hold-out

• Feature Engineering:

  • Create lagged variables for meteorological factors (e.g., rainfall with 1-month lag) [51]
  • Calculate spatial covariates (distance to water bodies, elevation, land use)
  • Derive interaction terms between key environmental and social variables

• Model Training:

  • Train multiple algorithms (BRT, RF, neural networks) using cross-validation
  • Optimize hyperparameters through grid or random search
  • For BRT, key parameters include: number of trees, learning rate, and tree complexity
  • For RF, optimize: number of trees, maximum depth, and minimum samples per leaf

• Model Evaluation:

  • Assess performance using appropriate metrics (AUC, accuracy, sensitivity, specificity)
  • Conduct spatial and temporal cross-validation to evaluate generalizability
  • Analyze variable importance to identify key drivers of transmission risk

• Prediction and Mapping:

  • Generate predictions for unsampled locations and time points
  • Create spatio-temporal risk maps at appropriate resolutions
  • Quantify prediction uncertainty through bootstrapping or Bayesian methods

Applications: This protocol has been successfully implemented for leptospirosis risk prediction in New Caledonia [51], identifying rainfall and humidity with one-month lag as significant contributors to Leptospira contamination.

Reproductive Number (Rt) Estimation

Purpose: To estimate the time-varying reproductive number (Rt) as a measure of transmission intensity to quickly assess whether infections are increasing or decreasing.

Materials:

• Time series of incident case counts, emergency department visits, or hospitalizations
• Data on serial intervals or generation intervals for the pathogen of interest
• Statistical software with Rt estimation capabilities (R packages EpiNow2, epinowcast)

Procedure:

• Data Preparation: Obtain daily counts of incident cases or syndromic surveillance data with minimal reporting lag.
• Generation Interval Specification: Define the distribution of time between successive cases in a transmission chain, typically based on prior epidemiological studies.
• Model Implementation: Use Bayesian methods to estimate Rt while adjusting for delays and reporting effects. The EpiNow2 package implements this approach using a flexible framework that accounts for uncertainty in the reporting process.
• Interpretation: Classify epidemic trends based on the probability that Rt > 1:
  • >90% of credible interval >1: Infections are growing
  • 76%-90%: Infections are likely growing
  • 26%-75%: Infections are not changing
  • 10%-25%: Infections are likely declining
  • <10%: Infections are declining [52]

Applications: The U.S. Centers for Disease Control and Prevention (CDC) uses this approach to track COVID-19, influenza, and RSV transmission trends at state and national levels [52].

Table 3: Essential Research Reagents and Computational Tools for Spatio-Temporal Modeling

Category	Item/Resource	Specification/Purpose	Example Applications
Laboratory Reagents	ELISA kits	Pathogen-specific antibody detection	Serosurvey data for FoI estimation [49]
	PCR reagents	Viral RNA/DNA detection and quantification	Case confirmation, viral load measurement
	Sequencing kits	Whole genome sequencing	Viral phylogenetics, mutation tracking
Data Resources	Syndromic surveillance data	Near real-time health indicators	Rt estimation [52]
	Remote sensing data	Environmental monitoring	Habitat suitability modeling [51]
	Viral sequence databases	Genomic data repository	Phylogenetic analysis [13]
Computational Tools	R packages (EpiNow2)	Real-time epidemic analysis	Rt estimation [52]
	Bayesian modeling software (Stan, INLA)	Advanced statistical modeling	Spatial and spatiotemporal analysis [45]
	Machine learning libraries	Predictive modeling	Risk prediction [51]
	GIS software	Spatial data analysis	Risk mapping, spatial interpolation

Application to Viral Zoonotic Potential and Species Jump Research

Predictive Modeling for Zoonotic Hotspots

The integration of spatio-temporal modeling with viral molecular data enables more precise identification of zoonotic hotspots - regions with elevated risk of cross-species transmission. Recent research applying phylogenetic factorization to bat viruses has revealed that high viral epidemic potential clusters within specific bat clades, with geographic distributions pointing to coastal South America, Southeast Asia, and equatorial Africa as regions of particular concern [13].

Critical to this approach is the move beyond binary classification of zoonotic potential (whether a pathogen can or cannot infect humans) toward multidimensional assessments that incorporate virulence (severity of disease), transmissibility (capacity to spread in human populations), and death burden (total human disease-induced mortality) [13]. This refined characterization allows for more targeted surveillance and resource allocation.

Limitations of Viral Prospecting Approaches

While surveillance in animal populations to detect novel viruses before they infect humans has been a major activity in pandemic preparedness, evidence suggests that viral prospecting has had limited impact on accelerating medical countermeasure development [48]. Several viruses posing known threats to people lack approved vaccines, and known viruses discovered in human patients before 2000 have caused most major 21st-century outbreaks.

Only 11 viruses were isolated in animals prior to causing clusters of cases in humans, out of approximately 250 viruses known to infect humans [48]. Knowledge of these viruses from animal sources prior to their first outbreaks in humans has not consistently translated to robust preparedness capacity, as evidenced by the limited vaccine availability for viruses like Zika and Rift Valley Fever despite their prior identification in animal reservoirs.

Alternative Approaches for Spillover Prevention

Given the limitations of viral prospecting, alternative approaches focusing on transmission interface management show promise for spillover prevention:

• Targeted Interventions: Reducing human-wildlife contact in high-risk settings through improved animal husbandry, wildlife trade regulation, and habitat protection.
• Integrated Surveillance: Strengthening human, animal, and environmental health surveillance systems to enable rapid detection of spillover events.
• Ecological Modeling: Using spatial and temporal data on land use change, climate, and biodiversity to predict areas at highest risk of emerging infectious disease events.
• Computational Prediction: Developing machine learning models that integrate viral molecular features with ecological and epidemiological data to prioritize viruses with highest spillover potential.

Spatio-temporal risk modeling integrated with viral molecular data represents a powerful approach for understanding and predicting infectious disease dynamics. The field has evolved from purely statistical models to incorporate advanced machine learning methods, Bayesian frameworks, and physics-informed neural networks, each offering distinct advantages for different modeling scenarios.

The most effective approaches combine multiple methodologies, leveraging the strengths of each while acknowledging their limitations. Bayesian methods excel in uncertainty quantification and incorporating prior knowledge, machine learning approaches handle complex interactions in high-dimensional data, and mechanistic models provide biological interpretability and physical consistency.

Future advancements will likely focus on real-time integration of diverse data streams, improved uncertainty quantification in predictions, and enhanced computational efficiency to enable more responsive public health decision-making. As climate change and anthropogenic pressures continue to alter host-pathogen dynamics, these modeling approaches will become increasingly essential for global health security.

The integration of viral molecular data with traditional epidemiological information provides a particularly promising path forward, enabling researchers to connect evolutionary processes at the molecular level with population-level transmission patterns across space and time. This multiscale understanding is essential for developing effective strategies to prevent, detect, and respond to emerging infectious disease threats in an interconnected world.

The One Health surveillance framework is an integrated, unifying approach that aims to sustainably balance and optimize the health of people, animals, and ecosystems. This approach recognizes that the health of humans, domestic and wild animals, plants, and the wider environment are closely linked and interdependent [53]. The critical need for such a framework is underscored by the fact that over 70% of human emerging infectious diseases are of zoonotic origin, with the majority being viral in nature [54] [55]. The 21st century has witnessed a dramatic increase in the emergence and re-emergence of viral zoonotic diseases, with pathogens such as SARS-CoV-2, Ebola virus, and highly pathogenic avian influenza demonstrating the devastating potential of cross-species transmission events [54] [5].

The phenomenon of zoonotic spillover—the transmission of a pathogen from a vertebrate animal to a human—represents a complex process that requires the alignment of ecological, epidemiological, and behavioural determinants [7]. Understanding this process is fundamental to the broader thesis on viral zoonotic potential and species jump research. Spillover events occur when animal viruses undergo genetic changes that render them newly able to infect humans, and in some cases, subsequently acquire the ability to spread efficiently among human populations [41]. The One Health approach provides the necessary foundation for studying these complex interactions through structured collaboration and coordination between human, animal, and eco-health systems [55].

This technical guide examines the core components, methodologies, and implementation strategies for developing a comprehensive One Health surveillance framework focused on predicting and preventing viral zoonotic spillover. By integrating data across human, animal, and environmental domains, this framework aims to enable earlier detection of potential zoonotic threats, more accurate risk assessment, and more effective intervention strategies to mitigate global health security threats.

Theoretical Foundations: Understanding Zoonotic Spillover Pathways

The Hierarchical Barrier Model of Spillover

Zoonotic spillover requires pathogens to overcome a hierarchical series of barriers to cause infection in humans. The probability of spillover is determined by interactions among factors that can be partitioned into three functional phases that describe all major routes of transmission [7]:

• Pathogen Pressure: The amount of pathogen available to the human host at a given point in space and time, determined by interactions among reservoir host distribution, pathogen prevalence, and pathogen release from reservoir hosts, followed by pathogen survival and dissemination.
• Exposure Dynamics: Human and vector behavior that determines the likelihood, route, and dose of pathogen exposure.
• Within-Host Susceptibility: Genetic, physiological, and immunological attributes of the recipient human host, together with the dose and route of exposure, that affect the probability and severity of infection.

Spillover can only occur when gaps align in each successive barrier within an appropriate window in space and time, which explains why zoonotic transmission is a relatively rare event despite constant human exposure to animal pathogens [7].

Distinguishing Spillover Events from Species Jumps

In zoonotic disease research, it is crucial to distinguish between two distinct phenomena:

• Spillover Events: Incidental human infections with zoonotic viruses to which humans are susceptible but do not efficiently transmit from human to human [41]. Examples include Marburgvirus and Hantavirus infections where humans become infected but typically do not transmit the virus efficiently to other humans.
• Species Jumps: Animal viruses undergo genetic changes that render them newly able to spread efficiently among humans [41]. Historical examples include HIV-1 (originating from SIVcpz in chimpanzees) and SARS coronavirus (originating from SARS-like coronaviruses in civets, raccoon dogs, and bats).

This distinction is critical for risk assessment and surveillance prioritization, as viruses that have spilled over into human populations may subsequently evolve to efficiently transmit among human hosts.

Pathogenesis Terminology in Zoonotic Research

To better conceptualize the differences in how zoonotic viruses behave across different hosts, we propose adopting the following specialized terminology [56]:

• Orthopathogenesis: The original pathogenesis in the reservoir host to which the virus has adapted.
• Neopathogenesis: The pathogenesis of a zoonotic viral infection in humans, representing a "new" or spillover event.
• Parapathogenesis: The pathogenesis observed in animals used to model human disease in experimental settings.

Comparing orthopathogenesis with neopathogenesis may reveal critical "tipping points" in the pathogeneses that explain differential disease outcomes and identify novel therapeutic targets to reduce severity in human cases [56].

Quantitative Risk Assessment Framework

The SpillOver Risk Ranking Tool

The SpillOver platform represents an innovative approach to systematically evaluate novel wildlife-origin viruses in terms of their potential for zoonotic spillover and spread in people [5]. This evidence-based risk assessment tool, developed through expert consultation and literature review, calculates a comparative "risk score" for each virus based on 31 key risk factors associated with spillover potential.

The SpillOver tool leverages data from testing 509,721 samples from 74,635 animals as part of a virus discovery project, creating a watchlist of potential pathogens that identifies targets for new virus countermeasure initiatives [5]. Validation of the risk assessment demonstrated that the top 12 ranked viruses were known zoonotic pathogens, including SARS-CoV-2, while several newly detected wildlife viruses ranked higher than some known zoonotic viruses, highlighting their potential threat.

Table 1: Key Risk Factors for Zoonotic Spillover Potential

Risk Factor Category	Specific Factors	Relative Influence Score
Virus-Related Factors	Virus family, mutation rate, mode of transmission, segmentation, envelope	High (2.5-3.0)
Host-Related Factors	Host plasticity, human interaction frequency/intimacy, reservoir host status	High (2.5-3.0)
Environmental Factors	Land-use change, climate shifts, globalization trends	Medium (2.0-2.5)
Human Behavior Factors	Wildlife trade, farming practices, cultural practices	Medium-High (2.2-2.8)

Methodological Approach to Risk Factor Analysis

The SpillOver risk assessment incorporates a weighted scoring system based on expert evaluation. A selection of 150 experts from 20 countries evaluated each risk factor in terms of influence on animal-origin virus spillover risk to humans [5]. The weighted average score for each risk factor was calculated from the sum of expert responses to Spillover Risk, accounting for the Level of Expertise of each expert within each subject using the formula:

Risk Factor Influence (0-3) = Σ(Spillover Risk (0-3) × Level of Expertise (1-16)) / ΣLevel of Expertise (1-16)

This methodological approach ensures that the risk ranking incorporates both scientific evidence and expert consensus, creating a robust foundation for prioritizing surveillance and research efforts.

Core Components of the One Health Surveillance Framework

Conceptual Framework for Integrated Surveillance

A comprehensive One Health surveillance framework requires infrastructure for coordinating, collecting, integrating, and analyzing data across sectors, incorporating human, animal, and environmental surveillance data, as well as pathogen genomic data [57]. The framework moves beyond traditional siloed surveillance systems to an integrated approach with the following core components:

• Multi-sectoral Coordination: Establishing formal governance structures with representatives from human health, animal health, and environmental sectors.
• Unified Data Integration Platform: Developing technical capacity for integrating heterogeneous data sources across domains.
• Shared Analytics and Interpretation: Implementing joint data analysis, reporting, and interpretation across sectors.
• Coordinated Response Mechanisms: Leveraging insights for timely, targeted interventions across the human-animal-environment interface.

The conceptual framework for One Health data integration emphasizes moving beyond scoping and planning to actual system development, production, and joint analyses, with special consideration for the complex partner identification, engagement requirements, and data governance challenges inherent in cross-sectoral work [57].

Operationalizing the One Health Approach

Successful implementation of the One Health surveillance framework requires addressing several operational components [55] [58]:

• Governance Structure: Establishment of formal governance bodies with representatives from each sector to overcome long-standing barriers of privacy and distrust.
• Interdisciplinary Training: Developing interdisciplinary training in One Health concepts for medical, environmental, and veterinary students to encourage cross-disciplinary collaboration.
• Economic Valuation: Demonstrating to policymakers the economic benefit of improved and timely detection of zoonoses to facilitate structured approaches.
• Political Will: Generating sustained political support through clear communication of the economic and public health benefits of integrated surveillance.

The New South Wales health service in Australia provides a successful case study, having achieved enhanced infection control and improved biosecurity procedures through the implementation of a single reporting system that integrates human and animal health data [55].

Experimental and Surveillance Methodologies

Predictive Surveillance Protocols

Predictive surveillance aims to identify ecological conditions that precede animal and human outbreaks and can provide timely warning to human populations [41]. The methodological approach includes:

• Virus Discovery Sampling:

  • Systematic collection of samples from wildlife at high-risk human disease transmission interfaces
  • Focus on known reservoir hosts (bats, rodents) and occupationally exposed humans
  • Use of consensus PCR protocols and metagenomic sequencing for pathogen detection

• Ecological Driver Monitoring:

  • Monitoring of land-use change, climate patterns, and wildlife population dynamics
  • Assessment of anthropogenic factors increasing human-animal interactions
  • Tracking of wildlife trade and agricultural practices that facilitate spillover

• Genomic Surveillance:

  • Pathogen genomic sequencing from human, animal, and environmental samples
  • Phylogenetic analysis to assess transmission dynamics at the human-animal-environment interface
  • Monitoring of genetic changes associated with host adaptation

Integrated Genomic Epidemiology

The application of pathogen genomic sequencing and analyses represents a transformative advancement for One Health surveillance [57]. Integrated genomic epidemiology combines genomic data from human, animal, and environmental sources to enable:

• Early Outbreak Detection: Identification of emerging threats through phylogenetic analysis of pathogen relationships across hosts
• Transmission Route Elucidation: Reconstruction of transmission pathways at the human-animal-environment interface
• Adaptation Signal Monitoring: Detection of genetic changes associated with host switching and increased human infectivity

Successful examples of integrated genomic surveillance systems include PulseNet, GenomeTrakr, and the European Food Safety Authority One Health Whole Genome Sequencing System, which have been predominantly applied in food-borne disease contexts but are increasingly being extended to zoonotic and vector-borne pathogens [57].

Table 2: Essential Research Reagents for One Health Surveillance

Reagent/Category	Primary Function	Application Examples
Pathogen Enrichment Reagents	Concentrate pathogens from complex samples	Viral transport media, bacteriologic enrichment broths
Nucleic Acid Extraction Kits	Isolate DNA/RNA from diverse sample types	Automated extraction systems for high-throughput processing
Metagenomic Sequencing Reagents	Enable unbiased pathogen detection	Library preparation kits, hybridization capture probes
Consensus PCR Primers	Detect known pathogen families	Pan-coronavirus, pan-filovirus primer sets
Serological Assay Antigens	Detect pathogen exposure in hosts	Recombinant viral proteins, whole-virus lysates
Cell Culture Systems	Isolate and propagate pathogens	Primary cells, organoids from multiple host species

Data Integration and Analytical Approaches

Technical Framework for Data Integration

Developing an integrated One Health data system requires addressing significant technical challenges, including data dispersion across domains, heterogeneous collection methods, lack of semantic interoperability, and complex data governance [57]. The technical framework includes:

• Data Collection Standards: Implementing standardized data collection protocols across human, animal, and environmental sectors.
• Semantic Interoperability: Developing shared ontologies and data models to enable cross-domain data integration.
• API-based Architecture: Creating application programming interfaces to facilitate automated data exchange between disparate systems.
• Cross-domain Analytics: Implementing analytical approaches that can accommodate complex data structures and relationships across domains.

The framework emphasizes the need for coordinated data integration at the response level, where surveillance data can directly inform public health, animal health, and environmental management decisions [57].

Analytical Methods for One Health Data

The complex, multi-domain data generated through One Health surveillance requires specialized analytical approaches [58]:

• Log-linear Models: Examine three or more variables and their inter-dependencies, permitting more than one outcome which is especially useful for complex One Health questions.
• Principal Component Analysis: Reduce dimensionality of complex datasets to identify key drivers of spillover risk.
• Structural Equation Modeling: Test complex networks of relationships among variables across human, animal, and environmental domains.
• Phylogenetic Diffusion Models: Reconstruct cross-species transmission events and evolutionary trajectories of pathogens.
• Multi-level Modeling: Account for nested data structures across different organizational levels and geographic scales.

These analytical methods enable researchers to move beyond simple correlations to understand the complex web of interactions that drive zoonotic spillover events.

Implementation Challenges and Solutions

Barriers to Integrated Surveillance

Implementation of One Health surveillance faces numerous challenges [55] [57]:

• Sectoral Silos: Human, animal, and ecological health are traditionally managed by separate sectors with limited communication and coordination.
• Data Governance Complexities: Data jurisdiction and organizational mandates differ between sectors, creating barriers to data sharing.
• Resource Limitations: Funding is often vertically allocated with limited resources available for cross-sector work, particularly at the local level.
• Technical Infrastructure Heterogeneity: Informatics capacity varies widely across systems, from paper data collection to complex systems with standardized automated reporting.
• Semantic Interoperability Gaps: Lack of common data standards and terminologies impedes data integration across domains.

Strategic Implementation Framework

Successful implementation requires a strategic approach that addresses these challenges [55] [58] [57]:

• Staged Implementation: Begin with pilot projects focused on specific high-priority pathogens or geographic areas before expanding to comprehensive surveillance.
• Stakeholder Engagement: Actively involve community members with on-the-ground experience, such as farmers, veterinarians, and park rangers, in surveillance design and implementation.
• Cross-sectoral Governance: Establish formal governance structures with representatives from all relevant sectors to coordinate surveillance activities and data sharing.
• Capacity Building: Develop interdisciplinary training programs to build One Health competencies across human health, veterinary, and environmental professions.
• Economic Advocacy: Quantify and communicate the economic benefits of integrated surveillance to secure sustained political and financial support.

The One Health surveillance framework represents a paradigm shift in how we approach the threat of emerging viral zoonoses. By integrating data across human, animal, and environmental domains, this framework provides a more comprehensive understanding of the complex interactions that drive zoonotic spillover and species jumps. The hierarchical barrier model of spillover, combined with quantitative risk assessment tools like SpillOver, provides a scientific foundation for prioritizing surveillance and research efforts.

Implementation of this framework requires addressing significant technical, governance, and operational challenges, but the potential benefits for global health security are substantial. As climate change, habitat loss, and increased human-animal interactions continue to elevate the risk of viral spillover [54] [59], the need for integrated, predictive approaches to zoonotic disease surveillance has never been greater. By moving beyond traditional siloed approaches to embrace a truly integrated One Health framework, we can enhance our ability to detect, prevent, and respond to emerging viral threats at the human-animal-environment interface.

Visualizations

Zoonotic Spillover Pathway

One Health Data Integration

The increasing frequency of viral emergences, from SARS-CoV-2 to panzootic H5N1 bird flu, underscores a critical need to shift from reactive to proactive pandemic preparedness [17] [60]. This transition hinges on our ability to operationalize predictions about which viruses possess the greatest zoonotic potential and are most likely to successfully jump species barriers. While the scientific foundation for such predictions has been advancing, the translation of this knowledge into actionable, real-world surveillance systems and intervention pipelines presents significant technical and operational challenges. This guide examines the current state of viral risk ranking platforms, details cutting-edge methodologies for early variant detection, and provides a framework for integrating these approaches into a cohesive strategy for targeted surveillance and countermeasure development, all within the critical context of viral zoonotic potential and species jump research.

Viral Risk Ranking Platforms: Capabilities and Limitations

Viral risk ranking platforms represent a first-generation approach to systematizing the assessment of zoonotic potential. The SpillOver tool, an open-source platform, exemplifies this strategy by evaluating wildlife-origin viruses using a weighted composite risk score derived from 31 risk factors [61]. These factors span virus, host, and environmental characteristics to assess a virus's potential to spill over from animals to humans and sustain transmission.

A critical reanalysis of the SpillOver platform, however, revealed a significant methodological concern: eight of its 31 risk factors depend on knowledge obtained after a spillover event has occurred (e.g., evidence of previous zoonotic spillover or sustained human transmission) [61]. The inclusion of these "spillover-dependent" risk factors introduces a degree of circularity, potentially inflating the perceived predictive power for novel viruses.

Table 1: Impact of Spillover-Dependent Factors on Risk Prediction Accuracy

Metric	Original Risk Score (Including Spillover-Dependent Factors)	Adjusted Risk Score (Excluding Spillover-Dependent Factors)
Area Under the Curve (AUC)	0.94	0.73
Key Predictive Risk Factors	Human infection, human transmission	Urbanization in host ecosystem, host plasticity, geographic range
Top-Ranked Viruses	Known human viruses (e.g., SARS-CoV-2, Ebola)	Novel coronaviruses & hantaviruses not yet known to spill over

This analysis demonstrates that while the original model excelled at classifying known human viruses (AUC=0.94), its performance declined when focused solely on pre-spillover factors (AUC=0.73) [61]. This underscores the necessity of refining these tools to rely exclusively on data available prior to a spillover event to be truly predictive for novel pathogens.

Advanced Computational Detection of Emerging Threats

Overcoming the limitations of static risk ranking requires dynamic, sequence-based surveillance methods capable of detecting emerging variants from genomic data in near real-time.

The HELEN Framework for Early Haplotype Detection

The HELEN (Heralding Emerging Lineages in Epistatic Networks) computational framework addresses the critical challenge of early viral variant detection by focusing on viral haplotypes—combinations of mutations—rather than individual mutations [62]. This is vital because selection often acts on combinations of mutations due to epistasis (non-additive interactions), a phenomenon prominently observed in SARS-CoV-2 variants like Omicron [62].

HELEN operates through a multi-stage analytical workflow designed to identify densely connected communities of co-occurring mutations that may represent emerging viral lineages long before they become prevalent.

Diagram 1: HELEN Framework Workflow

Key Methodological Steps:

• Network Construction: Builds a "coordinated substitution network" where nodes represent single amino acid variants (SAVs) and edges represent statistically significant co-occurrence of these SAVs in viral sequences, suggesting potential epistatic interactions [62].
• Community Detection: Applies graph theory algorithms to identify densely connected communities within the network. These dense communities represent groups of mutations that are evolving in a coordinated fashion [62].
• Haplotype Inference: Interprets these dense communities as candidate emerging haplotypes (viral variants) with high fitness potential.
• Cross-Validation: Validates the predicted haplotypes by confirming their presence in independently collected genomic data from different geographic regions, strengthening the predictive signal [62].

This approach allows HELEN to detect potential Variants of Concern (VoCs) like Omicron significantly earlier than traditional phylogenetic methods, which often identify lineages only after they have reached appreciable prevalence [62].

Machine Learning for Preemptive Therapeutic Development

Computational prediction also extends to developing countermeasures for known high-risk pathogens. For example, a machine learning pipeline named Rhodium was used to identify potential treatments for Nipah and Hendra viruses, which have high fatality rates in humans and significant pandemic potential [14].

Table 2: Machine Learning Pipeline for Antiviral Candidate Identification

Pipeline Stage	Description	Output/Result
Template Selection	Use of the structurally mapped measles virus (same family) as a blueprint for modeling.	A template for virtual screening.
Virtual Screening	Algorithmic screening of 40 million compounds for binding effectiveness to target viral structures.	A ranked list of candidate inhibitors.
Toxicity Filtering	Sifting out compounds with predicted toxic or adverse effects.	A refined shortlist of viable candidates.
Validation	In vitro testing of short-listed compounds in BSL-4 high-containment laboratories.	30 potentially viable viral inhibitors for Nipah and Hendra [14].

This pipeline demonstrates how computational methods can rapidly generate a shortlist of therapeutic candidates for dangerous pathogens that are difficult and resource-intensive to study in the lab, thereby accelerating preemptive drug development [14].

Experimental Protocols for Validating Zoonotic Potential

Computational predictions of spillover risk require validation through rigorous experimental protocols. The following section details key methodologies for assessing a virus's ability to infect human cells.

Receptor Tropism and Cell Entry Assay

This protocol determines whether a novel virus can use human receptor proteins to enter cells, a critical first step for zoonotic potential.

Objective: To assess the ability of a novel virus (e.g., a merbecovirus like HKU5) to utilize human host receptors (e.g., ACE2) for cell entry [63].

Materials:

• Virus-like particles (VLPs): Produced to carry the viral spike protein of the pathogen of interest but lacking the viral genome, enabling safe study of cell entry [63].
• Cell lines: Engineered to express reporter genes (e.g., luciferase) under a viral promoter, which are activated upon successful viral entry.
• Expression vectors: For cloning and expressing viral spike proteins.
• Human receptor-expressing cells: Cells engineered to stably express human receptors like ACE2, DPP4, etc.

Methodology:

• Spike Protein Cloning: The gene for the viral spike protein is cloned into an expression vector.
• VLP Production: Co-transfect cells with the spike protein vector and a packaging plasmid to generate non-replicating VLPs that display the spike protein.
• Infection Assay: Incubate the VLPs with target cell lines expressing different human receptors (e.g., ACE2, DPP4) and a control cell line.
• Quantification: After 48-72 hours, measure reporter gene activity (e.g., luminescence). A significant increase in signal in receptor-expressing cells compared to control cells indicates successful receptor-mediated entry [63].

Interpretation: A positive result suggests the virus has a baseline potential to infect human cells. For instance, this method confirmed that HKU5 viruses, closely related to MERS-CoV, can use bat ACE2 receptors but only weakly bind human ACE2, indicating they may be only a few mutations away from a more efficient human tropism [63].

Structural Prediction of Spike-Receptor Interaction

This protocol uses AI-based structural modeling to understand the molecular basis of receptor binding and predict mutations that could enhance zoonotic potential.

Objective: To model the 3D atomic-level interaction between a viral spike protein and a host receptor to identify key binding interfaces and potentially adaptive mutations [63].

Materials:

• AlphaFold 3 or similar software: An AI program for predicting the 3D structure of protein complexes.
• Protein sequences: Amino acid sequences of the viral spike protein and the host receptor.

Methodology:

• Input Preparation: Input the amino acid sequences of the viral spike and the host receptor (e.g., human ACE2) into the structural prediction software.
• Complex Prediction: Run the modeling algorithm to generate a predicted 3D structure of the spike-receptor complex.
• Interface Analysis: Analyze the model to identify specific amino acids in the spike protein that are critical for binding to the receptor.
• Mutational Analysis: In silico, introduce point mutations into the spike protein sequence and re-run the modeling to predict the structural and binding affinity consequences.

Interpretation: The model provides mechanistic insights into the barriers for zoonotic jump. For HKU5, this approach predicted specific mutations in the spike protein that could stabilize its binding to human ACE2, thereby flagging a potential evolutionary trajectory for the virus to watch for in surveillance [63].

The Scientist's Toolkit: Essential Research Reagents and Materials

Successfully operationalizing viral prediction requires a specific set of research tools and reagents. The following table details key components of the experimental pipeline.

Table 3: Essential Research Reagents for Viral Spillover Studies

Research Reagent / Material	Critical Function	Application Example
Virus-like Particles (VLPs)	Safe, non-replicating systems to study entry tropism of high-risk pathogens without requiring BSL-4 containment.	Studying cell entry of novel merbecoviruses like HKU5 [63].
Reporter Cell Lines	Quantify viral entry and replication efficiency via measurable signals (e.g., luminescence, fluorescence).	High-throughput screening of viral tropism for multiple human receptors.
Structural Modeling Software (e.g., AlphaFold 3)	Rapidly predicts 3D protein complexes to map binding interfaces and model mutational effects.	Mapping the interaction between HKU5 spike and bat/human ACE2 [63].
BSL-4 Laboratory	Provides the necessary high-containment environment for in vitro and in vivo work with uncharacterized or high-consequence pathogens.	Validating the infectivity of predicted high-risk viruses and testing antiviral candidates [14].
Phylogenetic & Phylodynamic Analysis Tools	Reconstruct evolutionary history, estimate emergence dates, and infer population dynamics from genetic sequence data.	Dating the origin of the HIV-1 pandemic and tracking global spread of influenza A [64].
1-(1-cyclopropylethyl)-1H-pyrazole	1-(1-Cyclopropylethyl)-1H-pyrazole	1-(1-Cyclopropylethyl)-1H-pyrazole (CAS 890591-87-4). A pyrazole-based compound for research use only (RUO). Not for human or veterinary diagnosis or therapeutic use.
KAN0438757	KAN0438757, MF:C21H18FNO7S, MW:447.4 g/mol	Chemical Reagent

Operationalizing predictions for viral spillover is a multi-faceted challenge that requires integrating disparate but complementary approaches. The path forward involves:

• Refining Risk Ranking Platforms by eliminating spillover-dependent factors to create truly predictive tools for novel viruses [61].
• Implementing Advanced Genomic Surveillance like the HELEN framework to detect emerging haplotypes from coordinated substitution networks early [62].
• Systematically Validating Predictions using robust experimental protocols for tropism and structural analysis to bridge computational predictions and biological reality [63].
• Developing Preemptive Countermeasures by leveraging machine learning and structural biology to identify broad-spectrum therapeutics against high-risk viral families [14].

By moving from static rankings to dynamic, sequence-driven surveillance and coupled experimental validation, the scientific community can build a more resilient global defense system against the next pandemic pathogen.

Navigating the Pitfalls: Challenges in Data, Surveillance, and Countermeasure Development

The majority of emerging and re-emerging infectious diseases in humans are caused by viruses that have jumped from animal populations, a process known as zoonosis [10]. Effective pandemic preparedness therefore hinges on our ability to detect and characterize viral threats before they establish chains of human transmission. Genomic sequencing has emerged as a powerful tool for understanding viral evolution and predicting zoonotic potential. However, the utility of this approach is fundamentally constrained by significant gaps in our current global viral surveillance infrastructure. This technical guide examines the precise nature and implications of these geographic and taxonomic biases through a quantitative lens, providing researchers with methodologies to address these limitations and more accurately assess the factors influencing viral host jumps.

Quantitative Analysis of Current Surveillance Biases

Systematic analysis of publicly available viral genomic data reveals profound disparities in both geographic and taxonomic sampling efforts. These biases create substantial blind spots in our ability to monitor viral evolution and pre-empt emerging threats.

Taxonomic Bias in Viral Sequencing

An analysis of ~12 million viral sequences hosted on NCBI highlights a pronounced focus on human-associated viruses, with domestic animals also receiving disproportionate attention compared to wildlife [10].

Table 1: Taxonomic Distribution of Vertebrate-Associated Viral Sequences on NCBI

Host Category	Approximate Percentage of Sequences	Notes
Human	93%	Dominated by SARS-CoV-2; reflects pandemic response sequencing [10]
Domestic Animals	15%*	Primarily Sus (pigs), Gallus (chickens), Bos (cattle), Anas (ducks) [10]
Other Vertebrates	9%*	Encompasses all other vertebrate genera; represents a massive surveillance gap
Note: Percentages for non-human hosts are calculated after excluding SARS-CoV-2 sequences.

This human-centric surveillance focus is further complicated by the discovery that humans may be as much a source as a sink for viral spillover, with more inferred viral host jumps from humans to other animals than from animals to humans [10]. This finding underscores the complexity of viral transmission networks and the limitation of a surveillance system that primarily observes a single node in this network.

Geographic Bias in Viral Sequencing

The collection of viral sequences from non-human vertebrates displays a strong geographical bias, leaving entire regions underrepresented in global databases [10].

Table 2: Geographic Biases in Viral Genomic Surveillance

Region	Surveillance Status	Implication
United States & China	Highly sampled	Surveillance efforts are concentrated in these countries [10]
Africa, Central Asia, South America, Eastern Europe	Highly underrepresented	Critical gaps exist in regions with high biodiversity and human-animal interface [10]
Global Pattern	Highly uneven	The bias varies significantly among the most-sequenced non-human host genera [10]

This uneven geographic coverage is particularly concerning given that factors like alterations in human-related land use are known risk factors for zoonotic emergence. Regions undergoing rapid ecological change are often precisely those where surveillance is weakest.

Impact of Metadata Quality

The utility of genomic data is compromised by poor metadata reporting. Approximately 45% of non-human viral sequences lack host information at the genus level, and 37% are missing sample collection year data [10]. This incomplete metadata severely constrains longitudinal studies and evolutionary analyses essential for understanding host-jump dynamics.

Methodologies for Enhanced Viral Host Prediction and Analysis

To overcome the limitations of biased surveillance data, researchers are developing sophisticated computational methods that can extract maximal information from available sequences and improve predictions about viral behavior.

Machine Learning for Host Prediction

Machine learning (ML) approaches have shown significant promise in predicting the hosts of novel viruses based on genomic features, which is particularly valuable when dealing with viruses from under-sampled hosts or regions.

Experimental Protocol: k-mer Based Host Prediction [65]

• Objective: To predict hosts of RNA viruses infecting mammals, insects, and plants using machine learning and short sequence k-mers.
• Data Collection: Complete genome sequences (N=17,482) of RNA viruses were downloaded from the Virus-Host Database. The final curated dataset consisted of 1,363 virus genomes after excluding arboviruses and removing closely related genomes (>92% identity).
• Sequence Features: Viral sequences were converted into numeric vectors using k-mer frequencies (length-normalized). Nucleotide k-mers (k=1 to 7) and amino acid k-mers (k=1 to 3, averaged over all proteins) were computed.
• Train-Test Splits: To rigorously evaluate performance on novel viruses, three partition scenarios were implemented:
  • Closely Related: All families equally represented in training and test sets.
  • Non-overlapping Families: Families in the test dataset were excluded from training.
  • Non-overlapping Genera: Genera in the test dataset were excluded from training (most challenging).
• Machine Learning Models: Four algorithms were trained and compared: Random Forest (RF), LightGBM (LGBM), XGBoost (XGB), and Support Vector Classifier (SVC). Hyperparameters were optimized via cross-validated grid-search.
• Key Finding: In the most challenging task of predicting hosts for unknown virus genera, the best model (SVC using 4-mer frequencies) achieved a median weighted F1-score of 0.79, a notable improvement over baseline homology-based methods (tBLASTx F1-score: 0.68) [65].

Deep Learning for Phage Sequence Identification

For prokaryotic systems, deep learning methods are advancing the identification of viral sequences within complex metagenomic data.

Experimental Protocol: HVSeeker for Host/Virus Classification [66]

• Objective: Distinguish bacterial and phage sequences from mixed metagenomes, a critical step in analyzing the viral components of samples.
• Data: Sequences were gathered from NCBI and IMGVR databases, comprising 536 bacterial and 2,687 phage DNA sequences.
• Data Preprocessing: Three distinct strategies were employed to handle sequences of varying lengths for the DNA-based model:
  • Padding: Cyclically repeating the sequence until it reaches a standard length (e.g., 1,000 bp).
  • Contigs Assembly: Combining multiple shorter sequences before splitting them into standard-length subsequences.
  • Sliding Window: Processing the genome with a moving window (e.g., 1,000 bp window moving 100 bp per step).
• Model Architecture: HVSeeker consists of two separate deep-learning models: one analyzing preprocessed DNA sequences and the other analyzing translated protein sequences, allowing for cross-verification.
• Performance: HVSeeker outperformed other methods (Seeker, DeepVirFinder, PPR-Meta) on benchmark datasets, particularly on sequences with lengths ranging from 200 to 1,500 bp, and showed improved performance in identifying unknown phage genomes [66].

Advanced Sequencing for Low Viral Loads

Sensitive detection in samples with low viral concentration—a common scenario in surveillance of animal hosts or human blood—requires optimized wet-lab protocols.

Experimental Protocol: NGS for Sensitive HBV Genome Analysis [67]

• Objective: Detect and generate full Hepatitis B virus (HBV) genomes from samples with low viral loads (0.2 to 6,207 IU/mL).
• Methods Compared: 9 established NGS methods from 6 European laboratories were blindly applied to 23 HBV-positive plasma samples. Methods included:
  • Untargeted metagenomics.
  • Pre-enrichment by probe-capture followed by Illumina sequencing.
  • HBV-specific PCR pre-amplification followed by sequencing (Nanopore or Illumina).
• Key Results:
  • PCR-Nanopore methods generated full genomes from samples with viral loads >10 IU/ml.
  • PCR-Illumina methods required >200 IU/ml.
  • Probe-capture methods (without pre-amplification) required >1,000 IU/ml and achieved limited genome characterization at low loads but could detect a wide range of other blood-borne viruses.
  • Untargeted metagenomics failed to generate full HBV genomes in any sample.
• Consideration: Contamination was observed in negative controls and very low viral load samples in all PCR-based methods, highlighting the need for stringent controls [67].

The Scientist's Toolkit: Essential Research Reagents and Solutions

The following table details key reagents, tools, and datasets critical for research in viral genomics and host prediction.

Table 3: Essential Research Reagents and Solutions for Viral Genomics

Item / Resource	Function / Application	Example / Source
Virus-Host Database	Provides curated taxonomic links between viruses and their hosts; essential for training and validation [65].	Virus-Host DB
NCBI Virus Database	Repository of viral sequence data; primary source for genomic data, though metadata quality is variable [10].	NCBI Virus
Probe-capture Panels	Enrichment of viral sequences from complex samples for sequencing; allows for broader virus detection [67].	Commercial panels (e.g., Twist Pan-Viral Panel)
Virus-Specific Primers	PCR pre-amplification to enable sequencing from very low viral load samples [67].	Custom-designed primers
k-mer Frequency Vectors	Numerical representation of viral genomes for machine learning models; captures compositional bias [65].	Custom scripts or tools like Jellyfish
Machine Learning Algorithms	Core engines for predictive models of host association or zoonotic potential [65] [66].	Scikit-learn (RF, SVC), XGBoost, LightGBM
Deep Learning Frameworks	Building complex models for sequence classification (e.g., CNN, LSTM) as in HVSeeker [66].	TensorFlow, PyTorch
Dlk-IN-1	Dlk-IN-1, MF:C20H24F3N5O2, MW:423.4 g/mol	Chemical Reagent

The geographic and taxonomic biases in viral sequencing are not merely logistical concerns; they represent fundamental weaknesses in our global early-warning system for pandemics. The quantitative data presented herein reveals a surveillance landscape that is profoundly human-centric and geographically clustered, leaving critical gaps in regions of high biodiversity and rapid environmental change. This skewed data directly impedes our ability to accurately assess the zoonotic potential of viruses and understand the evolutionary drivers of host jumps. However, as detailed in this guide, emerging computational methodologies—particularly machine and deep learning models trained on viral genome composition—offer powerful tools to extract more predictive signal from existing, imperfect datasets. Coupled with sensitive, targeted sequencing protocols for low-biomass samples, these approaches can help mitigate the current biases. Closing the surveillance gaps themselves must remain a global priority, but until then, leveraging advanced analytical frameworks is essential for strengthening our preparedness against the next viral threat.

The study of viral zoonotic potential—the ability of animal viruses to jump species and infect humans—increasingly relies on the secondary analysis of genomic data stored in public repositories. The value of this data for predicting and preventing pandemics is entirely contingent on the availability of rich contextual metadata that describes the host organism, and the time and place of sample collection. This contextual information enables researchers to track viral evolution, understand ecological niches, and identify hotspots for emerging pathogens. However, a pervasive crisis in metadata reporting is severely undermining these efforts, creating critical blind spots in our understanding of viral dynamics and hampering preparedness for future outbreaks. The genomic data itself, while essential, tells only part of the story; without comprehensive metadata, we lack the context necessary to interpret genetic sequences in relation to their environmental origins, host species, and temporal progression—all factors crucial for assessing zoonotic risk.

This metadata deficit represents more than a mere administrative oversight. It directly compromises the FAIR principles (Findable, Accessible, Interoperable, and Reusable) that underpin modern scientific discovery [68]. In the context of viral zoonosis research, where understanding host-pathogen-environment interactions is paramount, the consequences are particularly severe. Incomplete metadata obstructs the identification of patterns in viral emergence, hinders the reconstruction of transmission pathways, and ultimately delays the development of countermeasures against potential pandemic threats. As we confront an era of increasing zoonotic emergence, characterized by diseases such as COVID-19, Ebola, and avian influenza, addressing this metadata crisis becomes not merely an academic concern but an urgent imperative for global public health security.

Quantifying the Metadata Deficit

Documented Gaps in Genomic Repositories

The scale of missing metadata in genomic repositories is both vast and systematically documented. Analyses of major databases reveal startling gaps in essential contextual information. During the COVID-19 pandemic, a fundamental lack of metadata significantly challenged the scientific response. As of May 2020, 2,416 of 5,198 SARS-CoV-2 BioSample submissions (approximately 46%) lacked any annotation for the 'host' field, a fundamental descriptor for a pathogen sample [68]. Similarly, geographic origin metadata was missing for 25% of SARS-CoV-2 sequences in the Sequence Read Archive (SRA), while a broader analysis of 'viral metagenome' entries found 68% lacked country/continent information [68]. This deficiency persists beyond human pathogens to wildlife and environmental samples, crippling efforts to track viruses in animal reservoirs.

Table 1: Metadata Completeness in Select Genomic Studies and Repositories

Dataset	Scope	Key Missing Metadata	Completeness Rate
SARS-CoV-2 BioSamples (2020) [68]	5,198 submissions	Host information	54% (2,782 of 5,198 had host data)
SRA Viral Metagenomes [68]	~12,000 experiments	Geographic location (country/continent)	32% (only 32% had geolocname)
General Omics Studies [69]	253 studies, 164,000 samples	Phenotypic data (e.g., age, sex, tissue)	74.8% (average across studies)
GEO Repository [69]	61,000 studies, 2.1M samples	Core phenotypes (organism, sex, age, tissue)	63.2% (average availability)

The problem extends to consistency as well as completeness. In the same SARS-CoV-2 dataset, the 'host disease' field was populated with at least 11 different inconsistent terms (e.g., "COVID-19," "severe acute respiratory syndrome," "novel coronavirus pneumonia"), and over half of the submitted samples reported no disease at all [68]. This lack of standardization makes automated data integration and analysis extraordinarily difficult.

The Temporal Decay of Metadata Availability

A particularly alarming aspect of the metadata crisis is the phenomenon of metadata decay—the progressive loss of recoverable metadata over time. Research demonstrates that the probability of retrieving spatiotemporal metadata declines significantly as datasets age [70]. One study found a 13.5% yearly decrease in the recoverability of metadata from published papers or online repositories, and up to a 22% yearly decrease for metadata available only from the original authors [70]. This rapid decay effectively renders associated genetic data invisible for future macrogenetic studies and monitoring programs, representing the loss of millions of dollars in research investment and irreplaceable biological context.

Impact on Viral Zoonotic Potential and Species Jump Research

Impeding Pathogen Surveillance and Early Warning

Incomplete host and spatiotemporal metadata directly cripples our ability to identify and monitor viruses with high epidemic potential in their animal reservoirs. Research has shown that the threat is not uniformly distributed across species; for instance, bats from specific phylogenetic clades and geographic regions pose a disproportionately higher risk [13]. However, without accurate geographic coordinates and host taxonomy, scientists cannot effectively map these risk hotspots or target surveillance efforts. The lack of location data prevents researchers from correlating viral findings with environmental drivers of spillover, such as land-use change or climate variations [13]. Furthermore, the absence of collection dates stymies the study of viral evolution and transmission dynamics within reservoir populations, which is critical for understanding the adaptive potential of animal viruses to infect humans.

Undermining "One Health" Preparedness

The One Health approach, which recognizes the interconnected health of humans, animals, and ecosystems, is considered essential for addressing zoonotic threats [47]. Its effectiveness, however, depends on integrating data across these domains. The metadata crisis creates fundamental breaks in this data integration pipeline. For example, in the case of avian influenza (H5N1), which has moved from wild birds to poultry to dairy cows and finally to humans, incomplete metadata about the host species and inter-species contact at each jump makes it difficult to reconstruct transmission networks and implement targeted controls [47]. Similarly, for underdiagnosed diseases like leptospirosis, which is transmitted from rodents to humans through contaminated water, missing data on the environmental context and reservoir host prevalence hinders risk assessment and outbreak prevention [47]. This fragmentation of information across the human-animal-environment interface represents a critical vulnerability in our global health defense system.

Root Causes of the Metadata Crisis

Perceptual and Technical Barriers

Researchers face a complex array of barriers that contribute to poor metadata reporting. Perceptual barriers include a lack of awareness regarding the broader value of metadata for secondary research and a underestimation of the costs imposed on the scientific community when metadata is incomplete [68] [71]. There is also a persistent misalignment of incentives, where the considerable time and effort required for meticulous metadata curation is rarely rewarded in academic career advancement or publication opportunities [71].

On the technical side, challenges abound. Researchers often confront a lack of uniform standards or confusion over which metadata standards to use among many options [68] [71]. The process of formatting metadata to meet repository requirements is often manual, time-consuming, and requires specific expertise that may not be supported by research teams. Furthermore, concerns about privacy—particularly for human data or precise geographic locations—can lead to over-redaction and the submission of deliberately vague or missing metadata [68] [71]. These technical hurdles are compounded by inadequate data management infrastructure and a shortage of trained data managers within research labs.

Systemic and Policy Shortfalls

The problem is reinforced by systemic issues. While major repositories like the International Nucleotide Sequence Database Collaboration (INSDC) have implemented metadata requirements, their enforcement and validation have been inconsistent [68] [70]. For instance, the INSDC only recently moved to mandate collection date and country of origin, and this policy will not retroactively address the millions of existing datasets with missing context [70]. Journal policies that require data deposition as a condition of publication have successfully increased data sharing but have been less effective at ensuring the completeness and quality of the associated metadata [70] [69].

Solutions and Best Practices

Experimental Protocols for Robust Metadata Capture

To ensure genomic data is accompanied by FAIR (Findable, Accessible, Interoperable, and Reusable) metadata, researchers should integrate the following protocols into their workflows.

Protocol 1: Pre-Submission Metadata Auditing This protocol should be performed before submitting data to any public repository.

• Checklist Application: Use the relevant MIxS (Minimum Information about any (x) Sequence) checklist throughout the experimental process, not just at the point of submission [68].
• Standardized Vocabularies: Populate metadata fields using structured controlled vocabularies and ontologies (e.g., Environment Ontology, Disease Ontology) as specified in the MIxS standard to ensure consistency and interoperability [68].
• Internal Review: Conduct an internal audit using a pre-defined spreadsheet that cross-references every sample against all required metadata fields. Designate a team member to verify that no fields are left blank or filled with non-informative placeholders like "missing" or "not collected."

Protocol 2: Spatial and Host Metadata Annotation for Zoonosis Research This specialized protocol is critical for studies focused on viral zoonoses.

• Geographic Annotation: Record the GPS coordinates of the sample collection site. If privacy is a concern (e.g., for endangered species or private land), obscure the coordinates to a first-level administrative division (e.g., state/province) but retain the precise data under controlled access where justified [68].
• Host Pathogen Context: For host-associated samples, report:
  • Host species: Using formal binomial nomenclature and a linked taxonomy ID.
  • Host health status: Including specific signs of disease, using ontology terms where possible [68].
  • Host age, sex, and life stage: As these factors can influence viral susceptibility and shedding.
• Temporal Context: Record the exact date of collection (YYYY-MM-DD) to enable temporal trend analysis and evolutionary rate estimation.

The following workflow diagram visualizes the integration of these protocols into a robust research pipeline.

The Scientist's Toolkit: Key Research Reagent Solutions

Implementing the protocols above requires both conceptual tools and practical resources. The following table details essential components of a metadata management toolkit for researchers in viral zoonosis.

Table 2: Essential Toolkit for Managing Genomic Metadata

Tool or Resource	Type	Primary Function in Metadata Management
MIxS Checklists [68]	Standardization Tool	Provides the minimum set of fields required to describe genomic sequences for different environments (e.g., host-associated, water, soil).
Environment Ontology (ENVO) [68]	Ontology	Offers standardized terms for describing environmental habitats and conditions.
Disease Ontology (DO) [68]	Ontology	Provides consistent vocabulary for describing human and animal diseases.
FAIRsharing [68]	Educational/Registry Platform	Tracks and informs about metadata standards, databases, and policies, helping researchers select the right tools.
GEO Metadata Validator	Validation Tool	Checks metadata files for compliance with repository requirements before submission.
INSDC Biosample Template	Data Entry Tool	The structured template used by major repositories (NCBI, ENA, DDBJ) to collect sample information.

Systemic and Policy Interventions

Solving the metadata crisis requires more than individual researcher action; it demands systemic change. Funding agencies must require robust Data Management Plans (DMPs) that explicitly budget time and resources for metadata curation and provide supplemental funds for these activities [70] [71]. Scientific journals should strengthen their enforcement of metadata policies, using automated validators to check for completeness and adherence to standards before manuscript acceptance [68] [69]. Repository managers must continue to enhance user interfaces, providing clear guidance, templates, and immediate feedback to submitters. Finally, the research community must develop a culture of data stewardship that recognizes rich metadata as a first-class research output, critical to the integrity and utility of genomic science, particularly in high-stakes fields like pandemic preparedness [68] [70] [72].

The crisis of incomplete metadata in genomic repositories is not a peripheral administrative issue but a central failing that undermines a decade of investment in genomics and jeopardizes our ability to predict and prevent future pandemics. The lack of host and spatiotemporal data creates fundamental, often irreversible, gaps in our understanding of viral ecology and evolution. Addressing this crisis requires a concerted effort from individual researchers, institutions, repositories, and funders. By adopting standardized practices, implementing robust experimental protocols, and advocating for systemic change, the scientific community can transform genomic data from a fragmented collection of sequences into a truly powerful, predictive resource for understanding viral zoonotic potential and protecting global health.

Predicting genetic adaptation is pivotal for assessing viral zoonotic potential and understanding the mechanisms governing cross-species transmission. This whitepaper provides a technical guide on computational and experimental methodologies for identifying adaptive mutations in structural and auxiliary genes, with a specific focus on their implications for viral spillover and epidemic potential. We synthesize current data on viral traits, detail protocols for forward and reverse genetic approaches, and present a standardized framework for integrating multi-scale biological data. Aimed at researchers and drug development professionals, this resource underscores the necessity of a One Health approach to mitigate future pandemic risks.

Zoonotic diseases, which spread from animals to humans, represent over 70% of emerging infectious diseases and demand serious attention for their potential to spark pandemics, disrupt food supplies, and cause major economic damage [47]. The evolutionary leap of a virus from an animal host to humans hinges on its ability to adapt at the genetic level. This adaptation is governed by mutations in two primary gene categories: structural genes, which often code for proteins directly involved in host cell entry (e.g., viral capsid or envelope proteins), and auxiliary genes, which can modulate host immune responses, virulence, and environmental persistence [47] [13].

The One Health approach, which acknowledges the interdependent nature of human, animal, and environmental health, is critical for unraveling these complex transmission webs [47]. Furthermore, the presumption that all species within a reservoir taxon contribute equally to risk is inaccurate. For instance, within bats—a recognized reservoir of high-virulence viruses—epidemic potential is not uniformly distributed but clusters within specific clades, often composed of cosmopolitan families [13]. Accurately predicting adaptive mutations allows researchers to identify animal reservoirs with the highest viral epidemic potential, prioritize strains for surveillance, and accelerate the development of targeted medical countermeasures.

Quantitative Landscape of Viral Adaptation and Zoonotic Risk

A data-driven assessment is fundamental for prioritizing research and surveillance efforts. The following tables summarize key quantitative relationships between host species, viral traits, and genetic features that influence zoonotic adaptation.

Table 1: Association between Bat Clades and Measures of Viral Epidemic Potential in Humans. Data derived from phylogenetic factorization analyses of host-virus associations [13].

Bat Clade (Example)	Mean Case Fatality Rate (CFR) Association	Onward Transmission Association	Mean Death Burden Association	Key Viral Families
Pteropodidae (Flying foxes)	High	Moderate	High	Henipaviruses, Coronaviruses
Vespertilionidae (Vesper bats)	High	Low	Moderate	Coronaviruses, Lyssviruses
Phyllostomidae (Leaf-nosed bats)	Low	Not Significant	Low	Arenaviruses

Table 2: Forward vs. Reverse Genetic Approaches for Identifying Adaptive Genes [73].

Aspect	Forward Genetics (Top-Down)	Reverse Genetics (Bottom-Up)
Starting Point	Adaptive phenotypic variation (e.g., increased host cell infectivity)	Genomic regions with signatures of natural selection
Typical Methods	QTL mapping, GWAS, controlled crosses	Genome scans, transcriptome sequencing, environmental associations
Key Advantage	Agnostic to gene function; links directly to a measurable phenotype	Unbiased by prior phenotypic knowledge; can reveal "cryptic" adaptation
Major Challenge	Can miss traits with no obvious phenotypic correlate	Requires extensive functional validation to connect genotype to phenotype
Example in Virology	Mapping viral host-range mutations via plaque assay morphology	Identifying positively selected sites in viral receptor-binding proteins

Experimental Protocols for Predicting Genetic Adaptation

A combination of well-established and novel protocols enables the systematic identification and validation of adaptive mutations in structural and auxiliary genes.

Protocol 1: Genome-Wide Scans for Selection (Reverse Genetics)

Objective: To identify genes and non-coding regions under positive selection without a priori phenotypic knowledge.

Materials:

• High-Quality Genomes: Whole-genome sequences from multiple virus strains or host species.
• Computational Resources: High-performance computing cluster capable of running phylogenetic inference software (e.g., HYPHY, PAML, BUSTED).
• Annotation Database: A curated database of gene annotations (e.g., NCBI, VIRION [13]).

Methodology:

• Data Curation: Compile a whole-genome sequence alignment for the target virus across different host species or for a host species across its geographic range. Ensure high sequence quality and accurate annotation of open reading frames.
• Phylogenetic Inference: Reconstruct a robust phylogenetic tree using a model of nucleotide substitution. This tree represents the evolutionary relationships among the sequences.
• Selection Analysis: Apply site-specific (e.g., FEL, FUBAR) and branch-site (e.g., BUSTED, aBSREL) models to test for evidence of positive selection (dN/dS > 1). These models compare the rate of non-synonymous mutations (dN, altering the amino acid) to synonymous mutations (dS, silent).
• Validation: Candidate genes identified through genome scans must be validated experimentally. This involves techniques such as site-directed mutagenesis to introduce the putative adaptive mutation into a model viral genome, followed by phenotypic assays (see Protocol 2) to assess changes in replication efficiency, host cell entry, or immune evasion.

Protocol 2: Phenotypic Validation via Site-Directed Mutagenesis (Forward Genetics)

Objective: To functionally validate the impact of a specific mutation on viral fitness and virulence-associated phenotypes.

Materials:

• Reverse Genetics System: An infectious clone of the target virus.
• Cell Cultures: Relevant permissive cell lines (e.g., Vero E6, Caco-2, primary animal cells).
• Animal Models: Where appropriate and under high-containment biosafety levels (BSL-3/4), animal models (e.g., ferrets, humanized mice) to assess pathogenicity and transmission.
• Assay Reagents: qPCR kits for viral load quantification, ELISA kits for cytokine profiling, flow cytometry antibodies for immune cell characterization.

Methodology:

• Mutagenesis: Using the infectious clone as a template, engineer the specific mutation of interest using PCR-based or synthetic biology methods.
• Virus Recovery: Transfect permissive cells with the mutated genomic construct to recover infectious viral particles.
• In Vitro Characterization:
  • Growth Kinetics: Infect cells at a low multiplicity of infection (MOI) and titrate supernatant samples at regular intervals (e.g., 0, 12, 24, 48, 72 hours post-infection) to construct a one-step growth curve.
  • Plaque Assay: Determine viral titer and observe plaque morphology, which can indicate changes in cell-to-cell spread and cytopathicity.
  • Antibody Neutralization: Test the mutant virus's sensitivity to neutralization by convalescent serum or monoclonal antibodies to assess antigenic change.
• In Vivo Assessment (if applicable): Compare the pathogenicity of the wild-type and mutant virus in animal models. Key metrics include viral load in target tissues (e.g., lungs, brain), clinical scoring, histopathology, and evidence of transmission to co-housed animals.

Visualization of Research Workflows

The following diagrams map the logical flow of the integrated research strategies discussed in this guide.

Integrated Viral Adaptation Prediction Workflow

Host-Pathogen Interaction Network

Successful prediction and validation of viral adaptation require a suite of specialized reagents and databases.

Table 3: Key Research Reagent Solutions for Viral Adaptation Studies

Reagent / Resource	Function / Application	Example Use-Case
Infectious Clone System	Reverse genetics platform to genetically engineer and recover recombinant viruses.	Introducing a candidate mutation from a genome scan into a wild-type viral backbone for phenotypic testing.
Pseudotyped Virus Particles	Safe, single-cycle virus particles bearing a key viral envelope protein (e.g., Spike).	Measuring the efficiency of host cell entry for different viral variants in a BSL-2 setting.
Global Virome in One Network (VIRION) [13]	Comprehensive database of vertebrate-virus associations.	Identifying all known viruses for a target host clade and extracting their genomic sequences for analysis.
Species-Specific Primary Cells	Cell lines derived from the natural animal reservoir or human target tissue.	Assessing viral replication competence in a biologically relevant in vitro system.
Phylogenetic Analysis Software (e.g., HYPHY)	Statistical package for testing hypotheses of natural selection using genetic data.	Running FEL or BUSTED analyses on a viral gene alignment to find signatures of positive selection.
CRISPR-Cas9 Gene Editing System	Precision genome editing tool for modifying host cells or isogenic cell lines.	Knocking out a putative host receptor gene to validate its role in viral entry of a new variant.

The accelerating pace of viral emergence necessitates a proactive, predictive approach to zoonotic risk assessment. By integrating computational genomics—through both forward and reverse genetic approaches—with robust experimental validation in physiologically relevant models, researchers can move from merely documenting viral diversity to forecasting the adaptive pathways most likely to facilitate cross-species transmission. This technical guide provides a framework for deconvoluting biological complexity by focusing on the fundamental unit of evolution: the adaptive mutation. Prioritizing surveillance of animal reservoirs, particularly within identified high-risk clades, and preemptively characterizing the functional impact of mutations in structural and auxiliary genes, represents a critical strategy for pandemic preparedness and the development of broad-spectrum medical countermeasures [47] [13] [48].

Zoonotic diseases, pathogens that transmit from animals to humans, represent a profound and persistent threat to global health security. It is estimated that approximately 60% of emerging infectious diseases are zoonotic in origin, with wildlife serving as the primary reservoir for nearly three-quarters of these pathogens [74] [48]. The COVID-19 pandemic stands as a stark testament to the devastating potential of viral spillover events. Despite this recognized threat, significant gaps remain in the development of effective therapeutics and vaccines for many priority zoonoses, leaving the global community vulnerable to future outbreaks.

The challenges in developing countermeasures are multifaceted, arising from complex host-pathogen interactions, ecological dynamics, and technical constraints. This whitepaper examines the current landscape of vaccine and therapeutic development for zoonotic viruses, analyzes the persistent barriers, and outlines innovative platforms and strategies to address these critical gaps. Framed within the broader context of viral zoonotic potential and species jump research, this technical guide provides a comprehensive resource for researchers, scientists, and drug development professionals working to fortify our defenses against emerging viral threats.

Priority Zoonotic Pathogens and Countermeasure Gaps

The World Health Organization (WHO) and other public health agencies regularly assess and prioritize zoonotic pathogens with significant epidemic or pandemic potential. The updated WHO list of emerging pathogens reflects a strategic shift from focusing on specific pathogens to adopting a broader, family-level approach that incorporates 'Prototype Pathogens' and 'Pathogen X' - representing unknown pathogens capable of causing future epidemics [75]. This approach acknowledges the limitations of reactive pathogen-specific work and emphasizes preparedness for unfamiliar threats.

Table 1: Selected Priority Zoonotic Pathogens and Current Countermeasure Status

Pathogen/Virus Family	Disease	Transmission	Case Fatality Rate	Vaccine Status	Therapeutic Status
Filoviridae (Ebola, Marburg)	Ebola Virus Disease, Marburg Hemorrhagic Fever	Zoonotic, human-to-human	25-90% (varies by species)	Available for Ebola (rVSV-ZEBOV), none for Marburg	Limited (supportive care, some investigational therapies)
Nipah virus (Henipavirus)	Nipah virus infection	Zoonotic, limited human-to-human	40-75%	None licensed	Limited (monoclonal antibodies in development)
Zika virus (Flavivirus)	Zika virus disease	Vector-borne, sexual, maternal-fetal	Low, but severe birth defects	None	None
Rift Valley Fever virus (Phlebovirus)	Rift Valley Fever	Vector-borne, direct contact with infected animals	1% overall, 10% with hemorrhagic syndrome	None licensed	None
Avian Influenza viruses (H5N1, H7N9)	Highly pathogenic avian influenza	Zoonotic, limited human-to-human	~60% for H5N1, ~40% for H7N9	Candidate vaccines exist, not broadly deployed	Some antiviral susceptibility (oseltamivir)
SARS-CoV-2 (Coronavirus)	COVID-19	Zoonotic, efficient human-to-human	Varies by variant, population immunity	Multiple platforms available	Antivirals, monoclonal antibodies

Several viruses identified in Table 1 exemplify the critical gaps in our medical countermeasure arsenal. For Nipah virus, which has a case fatality rate of 40-75%, no licensed vaccines or therapeutics exist, creating a vulnerable scenario should this pathogen improve its human-to-human transmissibility [75]. Similarly, Rift Valley Fever virus continues to cause outbreaks in Africa without specific medical interventions, despite its potential for broader geographic spread due to climate change and vector distribution [47] [75]. The 2024 WHO priority pathogen list maintains focus on known threats with high mortality and transmission potential, including Crimean-Congo hemorrhagic fever, Ebola virus disease, Marburg virus disease, Lassa fever, MERS-CoV, SARS, Nipah and henipaviral diseases, Rift Valley fever, and "Disease X" - representing the unknown pathogen with pandemic potential [75].

Advances in Vaccine Platforms for Zoonotic Diseases

mRNA Vaccine Technology

The success of mRNA vaccines during the COVID-19 pandemic has revolutionized vaccine development, offering a highly adaptable platform for addressing emerging zoonotic threats. mRNA-based platforms provide significant advantages in rapid design, swift development, and the ability to elicit robust immune responses, making them particularly suitable for combating emerging and pre-pandemic zoonotic viruses [76] [74].

The fundamental principle of mRNA vaccines involves introducing synthetic mRNA encoding a target pathogen antigen into host cells, which then use their own translational machinery to produce the antigenic protein, eliciting both humoral and cellular immune responses. Key technological advances have been critical to this success:

• Lipid Nanoparticles (LNPs): Novel delivery systems that protect mRNA from degradation and enhance cellular uptake [74]
• Nucleotide Modification: Incorporation of modified nucleotides (e.g., pseudouridine) reduces innate immune recognition and enhances protein expression [74]
• Sequence and Structural Optimization: Codon optimization, untranslated region (UTR) engineering, and improved 5' capping and 3' tailing increase mRNA stability and translational efficiency [74]

Table 2: mRNA Vaccines Against Zoonotic Viruses in Development

Target Pathogen	Vaccine Candidate	Stage of Development	Key Antigen Target	Notable Findings
Rabies virus	CV7201, CV7202	Phase 1/2 clinical trials	Rabies virus glycoprotein (RABV-G)	Generally safe, dose-dependent immune responses; LNP formulation improved immunogenicity
Nipah virus	mRNA-1215	Phase 1 trials	Soluble glycoprotein (sG)	Preclinical studies showed complete protection in African green monkeys
Zika virus	mRNA-1893, mRNA-1325	Phase 2 trials	PrM-E proteins	Promising immunogenicity in early trials; advancement to larger trials pending
H5N1 Influenza	mRNA-1016, mRNA-1020	Preclinical/Phase 1	Hemagglutinin (HA)	Rapidly adapted to emerging clades; broad neutralizing antibody responses in animal models

Recent years have seen several mRNA vaccines targeting emerging and re-emerging zoonotic viral diseases advance to clinical trials, demonstrating promising immunogenicity and safety profiles [76]. The rapid design and manufacturing capabilities of mRNA platforms are particularly advantageous for "Disease X" scenarios, where traditional vaccine platforms would struggle to achieve timely deployment.

One Health Approach to Vaccine Development

The One Health framework, which recognizes the interconnected health of humans, animals, and ecosystems, provides an essential foundation for effective zoonotic disease vaccine development [47] [77] [78]. This approach emphasizes multisectoral collaboration and coordination across human, animal, and environmental health sectors to prioritize zoonotic diseases of greatest concern and develop targeted countermeasures [77].

The CDC's One Health Zoonotic Disease Prioritization (OHZDP) process brings together representatives from various sectors to collaboratively prioritize zoonotic diseases for targeted action [77]. This process has been implemented in numerous countries and regions, consistently identifying diseases such as rabies, zoonotic influenza viruses, and viral hemorrhagic fevers including Ebola as top priorities [77]. The OHZDP workshops not only generate prioritized disease lists but also create actionable recommendations and strengthen coordination mechanisms for multisectoral engagement.

Veterinary vaccines play a crucial role in the One Health approach to zoonosis control. As noted in research on avian influenza, "Ducks and geese are the main movers and shakers of the virus," highlighting the importance of understanding animal reservoirs and transmission dynamics for effective control strategies [47]. Vaccination in animal populations can reduce pathogen circulation at the source, potentially preventing spillover events into human populations.

Therapeutic Strategies for Zoonotic Viral Diseases

Innovative Antiviral Approaches

While vaccine development focuses on prevention, effective therapeutics are equally critical for managing outbreaks and treating established infections. Recent advances have expanded the antiviral arsenal beyond traditional small-molecule inhibitors to include novel mechanisms and platforms.

Defective Interfering Particles (DIPs) represent a promising emerging antiviral modality. DIPs are naturally occurring viral variants that contain significant deletions in their genomes, making them replication-incompetent. However, they can interfere with the replication of their parent viruses by competing for essential replication resources [79]. The biology of DIPs, their emerging applications as antiviral agents, and the challenges associated with their therapeutic use are currently under investigation as a novel class of antivirotics that could potentially revolutionize viral infection management [79].

Host-Directed Therapies offer another innovative approach by targeting host factors essential for viral replication rather than the virus itself. This strategy has the advantage of being less susceptible to viral mutation and potentially having broad-spectrum activity against related viruses. Research into host-virus interactions has identified numerous potential targets for therapeutic intervention, though this approach requires careful balancing of efficacy and host toxicity.

Natural Products and Repurposed Compounds

Natural compounds continue to provide valuable leads for antiviral development, offering diverse chemical structures and mechanisms of action.

Table 3: Promising Natural Compounds with Anti-Zoonotic Virus Activity

Compound	Source	Target Pathogen	Proposed Mechanism	Research Stage
Rosmarinic acid	Rosemary, other Lamiaceae plants	Chikungunya virus (CHIKV)	Modulation of IL-17 signaling pathway; suppression of viral replication	In vitro studies with network pharmacology validation
Melatonin	Endogenous hormone; synthetic	Bovine Viral Diarrhea Virus (BVDV) - model for related pathogens	Modulation of endoplasmic reticulum stress; downregulation of NF-κB signaling; regulation of autophagy	In vitro studies using MDBK cells
Star anise and cinnamon essential oils	Botanical sources	Multidrug-resistant Salmonella Thompson	Synergistic inhibition of bacterial growth and biofilm formation	In vitro antibacterial and antibiofilm assays

The study of combination formulations of star anise and cinnamon essential oils demonstrates the potential of synergistic natural compounds against multidrug-resistant zoonotic pathogens like Salmonella Thompson [79]. This approach offers a promising alternative to conventional antibiotics, addressing the critical issue of drug resistance in bacterial zoonoses.

Research on melatonin has revealed its unexpected antiviral properties against Bovine Viral Diarrhea Virus (BVDV), a significant animal pathogen with economic implications [79]. The study demonstrated that melatonin achieves its antiviral effect by modulating endoplasmic reticulum stress and downregulating the NF-κB signaling pathway, in conjunction with the regulation of autophagy [79]. This not only broadens the therapeutic profile of melatonin but provides a mechanistic basis for its potential application against related human pathogens.

Experimental Models and Methodologies for Zoonotic Disease Research

Surveillance and Viral Discovery

Effective surveillance forms the foundation of zoonotic disease preparedness, enabling early detection of potential threats and informing countermeasure development. Traditional surveillance has focused on viral prospecting - systematic efforts to detect novel viruses in animal hosts before they emerge in humans [48]. However, the utility of this approach for accelerating medical countermeasure development has been questioned.

A critical analysis of viral prospecting efforts found limited evidence that discovering novel zoonotic viruses in animal hosts before they cause human outbreaks has meaningfully accelerated vaccine or drug development [48]. Of approximately 250 viruses known to infect humans, only 11 were isolated in animals prior to causing clusters of cases in humans, and knowledge of these viruses from animal sources did not translate to distinctively robust capacity to prevent or respond to future outbreaks [48].

Alternative approaches gaining traction include:

• Focused surveillance on specific virus families with known zoonotic potential
• Integrated host-virus network analyses to identify hotspots of viral diversity and evolution
• Genomic sequencing of pathogens from outbreak investigations to guide rapid response

Recent research on bat reservoirs has revealed that viral epidemic potential is not uniformly distributed across the bat phylogeny [13]. Using phylogenetic factorization, researchers identified specific bat clades with unusually high or low viral epidemic potential, enabling more targeted surveillance efforts [13]. This approach allows for prioritization of surveillance in specific taxonomic groups and geographic regions, optimizing limited resources.

Genomic and Molecular Characterization Methods

Advanced genomic techniques have revolutionized our understanding of host-virus interactions and viral adaptation mechanisms. Whole genome sequencing coupled with phylogenetic analysis provides critical insights into viral evolution and spread, as demonstrated in surveillance of Avian Influenza A(H9N2) viruses in Senegalese live bird markets [80].

Key methodologies include:

• Whole genome sequencing using next-generation sequencing platforms
• Phylogenetic analysis to determine evolutionary relationships and transmission patterns
• Molecular characterization of key viral proteins and functional domains
• Selection pressure analysis to identify sites under positive or negative selection

In the Senegalese A(H9N2) study, genomic analysis revealed that isolates formed a monophyletic cluster and were closely related to a human strain (A/Senegal/0243/2019), suggesting cross-species transmission potential [80]. The strains possessed several key amino acid mutations associated with human host adaptation (HA-I155T, HA-Q226L), increased polymerase activity (PB2-T105V, PB2-A661T, PB2-A588V), and altered virulence (multiple NS mutations) [80].

Pathogen-Specific Experimental Workflows

Figure 1: Zoonotic Virus Research Workflow

The experimental workflow for zoonotic virus research begins with targeted surveillance informed by ecological and phylogenetic data, progressing through sample collection, molecular screening, genomic characterization, and functional studies before advancing to countermeasure development.

The Scientist's Toolkit: Essential Research Reagents

Table 4: Key Research Reagent Solutions for Zoonotic Virus Studies

Reagent/Category	Specific Examples	Function/Application	Technical Notes
Cell Lines	Vero E6, Huh-7, CaCo-2, A549, primary airway epithelial cultures	Viral isolation, propagation, and in vitro infection models	Select cell lines based on viral tropism and research objectives
Molecular Detection Kits	RT-qPCR kits (H5, H7, H9 influenza subtyping), LAMP assays	Pathogen detection and surveillance	LAMP offers field-deployable rapid detection; qPCR provides quantification
Sequencing Platforms	Illumina, Nanopore, PacBio	Whole genome sequencing, variant identification, evolutionary analysis	Nanopore offers portability and real-time sequencing
Antibodies	Monoclonal and polyclonal antibodies against viral proteins	Serological assays, immunohistochemistry, neutralization tests	Critical for antigen detection and functional studies
Animal Models	Mice (including humanized), ferrets, non-human primates	Pathogenesis studies, transmission experiments, therapeutic/vaccine testing	Species selection depends on viral tropism and research question
Protein Expression Systems	Mammalian, insect, bacterial cells	Recombinant antigen production for assays and vaccine development	Mammalian systems ensure proper post-translational modifications

Pathway Visualization: Host-Virus Interactions and Therapeutic Targeting

Figure 2: Host-Virus Interaction and Therapeutic Targeting

Understanding host-virus interactions at the molecular level is essential for developing targeted therapeutic interventions. Key signaling pathways, including NF-κB, endoplasmic reticulum stress response, autophagy, and IL-17 signaling, have been identified as critical determinants of viral replication and pathogenesis [79]. Therapeutic compounds such as melatonin and rosmarinic acid have demonstrated antiviral effects through modulation of these pathways, highlighting their potential as targets for host-directed therapy [79].

The landscape of therapeutics and vaccines for priority zoonoses is rapidly evolving, driven by technological advances and a growing recognition of the interconnected nature of human, animal, and environmental health. The mRNA vaccine platform represents a paradigm shift in rapid response capability, while novel antiviral approaches including defective interfering particles and host-directed therapies expand our options for treating established infections.

Significant challenges remain, particularly for pathogens with high mortality rates but limited commercial markets, highlighting the need for sustained public funding and innovative incentive structures for developers. The One Health approach provides an essential framework for coordinating efforts across sectors and disciplines, emphasizing that effective control of zoonotic diseases requires integrated strategies addressing the human-animal-environment interface.

Future progress will depend on continued advances in several key areas: (1) improved surveillance systems that integrate genomic, ecological, and epidemiological data; (2) platform technologies that can be rapidly adapted to novel threats; (3) better understanding of the molecular determinants of viral host switching and pathogenesis; and (4) strengthened global coordination mechanisms for equitable countermeasure development and deployment.

As climate change and human activities continue to alter ecosystems and increase human-wildlife interactions, the threat of emerging zoonoses will only intensify. By building on current scientific advances and addressing persistent gaps, the global research community can develop a more robust and responsive countermeasure arsenal to protect against future zoonotic threats.

The COVID-19 pandemic starkly revealed the critical importance of transforming scientific research into actionable intelligence for pandemic prevention and response. This whitepaper examines the multifaceted barriers to achieving actionable science in the specific context of viral zoonotic potential and cross-species jump research. Actionable science is defined as research that is not merely academically sound but is directly usable by decision-makers, policymakers, and frontline healthcare workers to inform real-world interventions. Despite advances in virology and computational biology, a significant "usability gap" persists between fundamental research on viral zoonoses and its practical application for preventing spillover events and mitigating outbreaks [81]. This gap is particularly problematic given the steady rise in zoonotic diseases, driven by factors such as climate change, habitat encroachment, and globalized travel, which collectively increase interactions between humans, animals, and pathogens [47]. Addressing these barriers is not merely an academic exercise but an urgent imperative for global health security. This document provides a technical analysis of these barriers, presents detailed experimental methodologies for key research areas, and proposes integrated solutions aimed at bridging the gap between viral discovery and deployed public health tools.

Institutional and Structural Barriers

The journey from fundamental viral research to actionable health outcomes is fraught with institutional and structural obstacles that often stymie even the most scientifically robust projects. Research indicates that the knowledge of what is required for success is often present among scientists, but the institutional context frequently makes it impossible to implement these best practices [81].

Table 1: Typology of Barriers to Actionable Zoonotic Science

Barrier Category	Specific Challenges	Impact on Zoonotic Research
Economic & Resource	Limited sustained funding, high startup costs for tool development, resource constraints in high-risk regions [82].	Hinders surveillance capacity in biodiversity hotspots, limits development of affordable diagnostics, and prevents long-term cohort studies of viral dynamics.
Technical & Capacity	Inadequate laboratory infrastructure, insufficient training in One Health approaches, lack of digital integration [83].	Impairs accurate pathogen identification, delays outbreak reporting, and creates data silos that prevent a unified view of spillover risks.
Political & Bureaucratic	Reporting reluctance due to economic concerns, complex international regulations, publication biases [83].	Leads to underreporting of outbreaks in animal populations, delays data sharing, and prioritizes academic publication over public health utility.
Professional & Intellectual	Automation bias in AI tools, lack of model explainability, liability concerns with predictive models [82].	Fosters mistrust of computational predictions of zoonotic potential, discourages clinical adoption of decision support tools, and slows iterative improvement.
Behavioral & Social	Resistance among agricultural producers to report outbreaks, lack of community engagement, insufficient cross-disciplinary collaboration [83].	Limits early detection in livestock populations, reduces effectiveness of interventions, and perpetuates fragmented approaches to complex problems.

A critical analysis of outbreak reporting systems reveals that technical barriers are consistently reported across all regions, but particularly affect low-resource settings where zoonotic surveillance is most needed [83]. Furthermore, there is often resistance to reporting among agricultural producers and other stakeholders who fear economic losses from control measures such as culling, creating a significant disconnect between detection and disclosure [83]. This is compounded by the "black box" nature of many advanced prediction models, which lack explainability and can perform unexpectedly under specific conditions, generating mistrust among end-users in public health [82].

Methodological Framework for Viral Zoonotic Risk Assessment

Integrated One Health Surveillance Protocol

A robust methodology for assessing viral zoonotic risk requires an integrated One Health approach that simultaneously examines human, animal, and environmental components. The following protocol provides a standardized framework for such assessments.

Table 2: Experimental Protocol for Integrated Viral Surveillance

Protocol Step	Methodology Details	Key Outputs
1. Sample Collection	- Human: Sera, nasopharyngeal swabs from patients with undiagnosed febrile illness.- Animal: Longitudinal sampling of wildlife (bats, rodents), domestic animal sera, and tissue from sick animals.- Environmental: Water, soil, and aerosol samples from human-animal interfaces [47].	Biobanked samples with complete metadata using standardized data fields (species, location, date, clinical signs).
2. Molecular Screening	- Nucleic acid extraction using automated systems for consistency.- Pan-viral consensus PCR targeting conserved regions of viral families (e.g., Coronaviridae, Filoviridae).- High-throughput sequencing (metagenomic and metatranscriptomic) for unbiased pathogen discovery [47].	cDNA libraries, sequence reads, and preliminary taxonomic classification of detected viruses.
3. Data Integration	- Geographical Information Systems (GIS) to map detection hotspots.- Statistical analysis of temporal patterns and association with environmental drivers.- Phylogenetic analysis to identify novel viral lineages and assess recombination potential.	Integrated database linking pathogen detection, host, and environmental data for risk modeling.
4. Risk Prioritization	- Application of machine learning algorithms to genetic features associated with human adaptability (e.g., codon usage, GC content, specific receptor-binding motifs).- Experimental validation of cellular tropism using pseudotyped virus systems.	Prioritized list of viruses with greatest spillover potential for further characterization.

This protocol emphasizes community engagement throughout the process, as successful surveillance in rural areas depends on trust and mutual benefit with local populations [47]. The methodological framework requires expertise from diverse disciplines including ecology, virology, bioinformatics, and veterinary medicine, embodying the One Health approach in its implementation.

Workflow Visualization: Integrated Pathogen Surveillance

Technical Hurdles in Model Validation and Decision Support

Barriers to Adoption of Predictive Tools

Clinical and ecological prediction models face significant adoption barriers that extend beyond their technical accuracy. Interviews with providers and tool creators have identified several key categories of obstacles that prevent effective deployment of predictive tools for zoonotic risk assessment [81] [82].

Table 3: Barriers and Solutions for Predictive Model Adoption

Barrier Category	Specific Challenges	Recommended Solutions
Economic	High development and maintenance costs; uncertain return on investment; limited funding for tool updates [82].	Cost-effectiveness analyses; modular design to reduce update costs; sustained funding mechanisms beyond pilot phases.
Practical	Poor integration into existing workflows; lack of stakeholder buy-in; insufficiently actionable outputs [82].	User-centered design; iterative testing with end-users; integration with electronic health records and surveillance systems.
Professional	Liability concerns; algorithmic flagging of clinical deviations; potential degradation of clinical reasoning [82].	Clear liability frameworks; model explainability; emphasis on decision support rather than decision replacement.
Intellectual	"Black-box" recommendations; automation bias; failure to account for local contextual factors [82].	Explainable AI techniques (LIME, SHAP); continuous validation; user training on appropriate interpretation.

Tool creators often understand the concepts necessary for developing actionable science and value stakeholder engagement throughout the design process, but face institutional barriers that prevent them from working in ways they know would be more effective [81]. This mismatch between knowledge and institutional possibility represents a critical structural challenge in the field.

Principles for Actionable Predictive Models

To overcome these barriers, developers of predictive models for zoonotic risk should adhere to eight core principles derived from implementation science [82]:

• Intended Use: Models should be designed to meet an a priori clinical or public health need identified through engagement with all stakeholders, including end-users in resource-limited settings.
• Usability: Tools must have low error frequency, visual appeal, and seamless integration into existing workflows, which may vary significantly across different healthcare and field settings.
• Explainability: Implement explainable AI techniques (LIME, SHAP, Grad-CAM) to clarify how models derive predictions, building trust and enabling appropriate interpretation.
• External Validity: Models must be validated on diverse external datasets representing different geographical regions and host populations, and continuously monitored for "data shift" as viruses evolve.
• Value: Document full development costs and demonstrate measurable patient or public health benefits through cost-effectiveness analyses.
• Safety and Fairness: Conduct algorithmic audits to ensure stable performance and promote equal distribution of care across different populations and regions.
• Regulation: Comply with evolving FDA guidance for software as a medical device, including premarket approval and adverse event reporting where applicable.
• Liability: Establish clear liability frameworks through consultation with legal experts before deployment.

Essential Research Toolkit for Viral Zoonotic Studies

Table 4: Research Reagent Solutions for Viral Zoonotic Studies

Reagent/Category	Specific Examples	Function/Application
Sample Collection & Stabilization	- Viral transport media (VTM)- RNAlater stabilization reagent- FTA cards for nucleic acid preservation	Maintains viral integrity and nucleic acid stability during transport from remote field sites to laboratories, critical for accurate sequencing.
Nucleic Acid Extraction & Amplification	- Automated extraction systems (QIAcube)- Pan-viral consensus PCR primers- Reverse transcriptase for RNA viruses	Enables detection of known and novel viruses from diverse sample types; foundation for downstream sequencing and characterization.
Sequencing & Library Prep	- Illumina Nextera XT for metagenomics- Oxford Nanopore kits for field sequencing- Target enrichment probes for specific viral families	Facilitates unbiased pathogen discovery and genomic characterization directly from clinical and environmental samples.
Cell Culture & Viral Propagation	- Various cell lines (Vero E6, A549, primary human airway epithelium)- Viral growth media with TPCK-trypsin- Antibiotic-antimycotic solutions	Allows for virus isolation and expansion for further study; essential for determining infectivity and host range.
Pseudotyped Virus Systems	- Lentiviral backbone plasmids- Viral glycoprotein expression vectors- Luciferase or GFP reporter constructs	Enables safe study of viral entry mechanisms and cellular tropism without requiring BSL-3/4 containment; critical for assessing spillover potential.
Serological Assays	- Recombinant viral antigens- Protein expression systems (e.g., baculovirus)- ELISA and neutralization assay components	Detects prior infection and immune responses in human and animal populations; measures cross-reactivity between related viruses.
Bioinformatic Tools	- CZ-ID (Chan Zuckerberg ID) pipeline- Nextstrain for phylogenetic analysis- BLAST and HMMER for sequence comparison	Provides computational framework for analyzing sequence data, tracking viral evolution, and identifying novel pathogens.

This toolkit represents the essential materials required for comprehensive studies of viral zoonotic potential, from initial field detection through mechanistic characterization. The selection emphasizes reagents that enable safe study of dangerous pathogens and facilitate standardized comparisons across studies and geographical regions.

Capacity Building and Global Coordination Framework

Addressing Disparities in Outbreak Reporting

Significant disparities exist in outbreak reporting capabilities worldwide, with technical and economic barriers being particularly pronounced in regions where zoonotic emergence events are most likely to occur. A scoping review of outbreak reporting barriers found that the East Asia and Pacific and Sub-Saharan Africa regions were the most studied, with technical barriers being consistently identified across all sectors [83]. The review, which examined 5,177 records and included 151 studies for analysis, found that only 45 studies evaluated outbreak reporting with respect to a specific disease, highlighting a critical gap in disease-specific reporting guidance [83].

The barriers to outbreak reporting fall under three major themes: (1) technical; (2) economic, political, and bureaucratic; and (3) behavioral and social [83]. While technical capacity building remains essential, a comprehensive strategy must also address the economic and political disincentives to reporting, particularly the resistance among agricultural producers who may suffer economic losses from control measures [83]. This requires sensitizing reporters and government officials on the long-term benefits of early reporting and developing compensation mechanisms that mitigate short-term economic impacts.

Workflow Visualization: Outbreak Reporting Pathway

Bridging the gap between viral zoonotic research and actionable public health interventions requires a systematic approach that addresses barriers across the entire research-to-implementation pipeline. Based on our analysis, we recommend the following priority actions:

• Implement Integrated One Health Surveillance that simultaneously monitors human, animal, and environmental health, using standardized protocols and data-sharing platforms to create a unified view of zoonotic threats.

• Develop Explainable, Context-Adapted Prediction Tools that incorporate stakeholder input from the outset, validate models across diverse settings, and prioritize interpretability to build trust and facilitate appropriate use.

• Address Institutional and Political Barriers through sustained funding mechanisms, clear liability frameworks, and economic incentives that encourage early reporting rather than penalizing it.

• Strengthen Global Capacity with a Focus on Equity by investing in laboratory infrastructure, bioinformatic capabilities, and training programs in regions with high zoonotic emergence risk, ensuring that all countries can participate effectively in global health security.

• Foster Cross-Disciplinary Collaboration that breaks down silos between human medicine, veterinary science, ecology, computational biology, and social sciences to develop comprehensive solutions to complex zoonotic threats.

The rising incidence of zoonotic diseases, from avian influenza in dairy cows to the expanding range of Lyme disease, underscores the urgency of this mission [47]. By transforming how we produce, validate, and implement zoonotic research, we can build a more proactive and effective global defense against the pandemics of tomorrow.

Case Studies and Framework Validation: Learning from High-Impact Zoonotic Viruses

Understanding the transmission dynamics of respiratory viruses is a cornerstone of public health preparedness and pandemic prevention. This is particularly critical within the context of viral zoonotic potential, as the majority of emerging infectious diseases originate from animal reservoirs [13]. Paramyxoviruses, influenza viruses, and coronaviruses represent three major families of respiratory pathogens with significant epidemic and pandemic potential. Each exhibits distinct strategies for host invasion, spread, and persistence within human populations. This whitepaper provides a comparative analysis of their transmission dynamics, framing these characteristics within the broader paradigm of species jump research. It is designed to equip researchers, scientists, and drug development professionals with a synthesized overview of key epidemiological data, experimental approaches for studying viral spread, and essential research tools for investigating cross-species transmission.

Comparative Epidemiology and Transmission Dynamics

The transmission potential of a virus is quantitatively summarized by the basic reproduction number (R0), which defines the average number of secondary infections generated by a single primary case in a fully susceptible population. The following table synthesizes key epidemiological parameters for the three virus families, highlighting differences in their transmission efficiency and population impact.

Table 1: Key Epidemiological Parameters of Paramyxoviruses, Influenza, and Coronaviruses

Virus Family	Representative Pathogens	Basic Reproduction Number (R0)	Primary Transmission Routes	High-Risk Populations
Paramyxoviruses	Respiratory Syncytial Virus (RSV), Human Parainfluenza Virus (HPIV), Human Metapneumovirus (hMPV)	Not well quantified for all; often shows biennial/out-of-phase patterns [84]	Droplet, Aerosol, Contact [85]	Young children, elderly [86]
Influenza	Seasonal Influenza A/H1N1, A/H3N2, Influenza B	H1N1: ~1.25; Seasonal: 2.2-3.6 [87]	Droplet, Aerosol, Contact [85]	Young children, elderly, immunocompromised [88]
Coronaviruses	SARS-CoV-2 (COVID-19), SARS-CoV, MERS-CoV	SARS-CoV-2: 1.4-3.8 (ancestral strain); variants can be higher [87]	Droplet, Aerosol, Contact [89]	Elderly, individuals with comorbidities [89]

Beyond R0, other quantitative metrics help differentiate the spread of these viruses. Age-structured attack rates reveal groups that act as key drivers of transmission. For instance, a detailed study on a remote island population in Japan confirmed that pre-school and school-aged children are the groups most at risk for influenza infection, with the highest relative illness ratios (RIRs) [88]. Similarly, the temporal dynamics of an outbreak, captured by the effective reproduction number (Rt), demonstrate the impact of interventions. During the 2021 COVID-19 surge in Taiwan, strict public health measures reduced the Rt from an initial 2.0–3.3 to 0.6–0.7, effectively bringing the epidemic under control [89].

A critical aspect of transmission dynamics is viral interaction. Research on paramyxoviruses has shown that cross-immunity between different strains can explain complex out-of-phase annual and biennial circulation patterns of RSV, HPIV, and hMPV. The strength of this cross-protection is correlated with the genetic distance between viruses in the paramyxovirus family [84].

Table 2: Comparative Viral Factors Influencing Transmission and Zoonotic Potential

Factor	Influenza Virus	Coronavirus (SARS-CoV-2)	Paramyxovirus
Receptor Usage	Sialic acids (α2,6-linked human preference) [85]	Angiotensin-converting enzyme 2 (ACE2) [90]	Sialic acids; variation in linkage preference (e.g., α2-3) and protein receptors [91]
Genetic Structure	Segmented, single-stranded RNA genome [90]	Non-segmented, single-stranded positive-sense RNA genome [90]	Non-segmented, single-stranded negative-sense RNA genome [91]
Key Zoonotic Trait	Antigenic shift/drift; broad avian and mammalian host range [47]	Broad host range via ACE2 conservation; recombination potential [13]	High viral diversity in specific bat clades; immune tolerance in reservoir hosts [13]

Experimental Protocols for Studying Transmission Dynamics

Reconstructing Transmission Chains from Epidemiological Data

Objective: To infer probabilistic "who-infected-whom" networks from surveillance data in the absence of direct contact-tracing or genetic sequencing, thereby identifying factors associated with onward transmission risk.

Background: This method is valuable for analyzing outbreak data from closed or semi-closed populations, such as islands or isolated communities, where importation events are limited and can be identified [88].

Methodology:

• Data Collection: Compile a line list containing, at a minimum, for each confirmed case: age, sex, date of symptom onset, geographic location (e.g., postcode), and pathogen type (e.g., influenza A/B). Imported cases should be flagged based on travel history [88].
• Relative Illness Ratio (RIR) Calculation: Calculate RIRs by age group to identify groups with excess infection risk. The formula for age group i is [88]: RIR_i = (C_i / ∑C_j) / (N_i / ∑N_j) where C_i is the number of cases in age group i, and N_i is the population of age group i.
• Transmission Tree Reconstruction: Use a Bayesian inference framework with Markov-chain Monte Carlo (MCMC) sampling to reconstruct the probabilistic transmission tree. This method estimates the likelihood that case i infected case j based on their relative symptom onset dates and spatial proximity, incorporating known or estimated serial interval distributions [88].
• Regression Analysis: Perform a negative binomial regression on the inferred number of secondary cases for each individual in the reconstructed trees. The model assesses the association between the number of secondary cases and covariates such as age, geographic district, local vaccination coverage, and virus type to identify drivers of transmission [88].

Quantifying Multivalent Virus-Receptor Interactions Using Nanoparticles

Objective: To elucidate the dynamics of multivalent receptor-binding and -destroying activities of viral surface proteins, which are key determinants of host tropism and virion motility, without requiring high-titer virus stocks.

Background: Sialoglycan-binding viruses like paramyxoviruses and influenza use low-affinity, multivalent interactions for motility, balanced with receptor-destroying activity (neuraminidase) to escape decoy receptors. Studying this balance is complicated by biosafety concerns and the difficulty of growing clinical isolates [91].

Methodology:

• Protein Production: Express and purify soluble, recombinant tetrameric hemagglutinin-neuraminidase (HN) proteins for paramyxoviruses (or HA/NA for influenza) with a C-terminal His-tag [91].
• Nanoparticle Conjugation: Incubate the purified His-tagged glycoproteins with Nickel-Nitrilotriacetic acid (Ni-NTA)-functionalized nanoparticles (NPs). This creates HN-NPs (or HA/NA-NPs) that multivalently display the viral glycoproteins, mimicking the avidity of a whole virion [91].
• Biolayer Interferometry (BLI) Assay:
  • Immobilize a sialoglycan receptor (e.g., 3'Sialyl-N-acetyllactosamine) on a BLI biosensor tip.
  • Dip the tip into a solution containing the HN-NPs to measure association kinetics as the particles bind the receptor.
  • Transfer the tip to a buffer solution to measure dissociation kinetics. The presence of active neuraminidase will cleave receptors, leading to faster dissociation and enabling the observation of receptor-destroying activity and virion motility in real-time [91].
• Kinetic Analysis: Compare association/dissociation curves for different virus strains or specific site-directed HN mutants to understand how genetic variations affect the dynamic receptor interactions and how this balance is adapted to host-specific sialoglycomes [91].

Diagram 1: Workflow for HN-NP BLI Assay

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Key Research Reagent Solutions for Viral Transmission Studies

Reagent/Material	Function/Application	Example Use-Case
Ni-NTA Nanoparticles	Platform for multivalent display of His-tagged viral glycoproteins, mimicking virion surface avidity [91]	Studying multivalent receptor interactions of paramyxovirus HN proteins without live virus [91]
Biolayer Interferometry (BLI)	Label-free technology for real-time analysis of biomolecular interactions (binding affinity, kinetics, avidity) [91]	Quantifying the dynamic binding and receptor-destroying activity of virus-like particles (HN-NPs) [91]
Multiplex Real-Time PCR Panels	Simultaneous detection and differentiation of multiple respiratory viral pathogens in a single patient sample [86]	Surveillance of viral co-circulation and co-infection, e.g., in pre-/post-pandemic studies [86]
Rapid Diagnostic Tests (RDTs)	Point-of-care or laboratory-based immunochromatographic assays for viral antigen detection [88]	Rapid case confirmation and large-scale surveillance data collection for epidemiological modeling [88]
Sialoglycan Receptors	Defined synthetic or purified natural receptors (e.g., 3'SLN, 6'SLN) for viral attachment studies [91]	Probing virus-receptor specificity and affinity in BLI or other binding assays [91]

The distinct transmission dynamics of paramyxoviruses, influenza, and coronaviruses are a direct reflection of their molecular biology and evolutionary history. A critical finding in species jump research is that the potential for viral emergence is not uniform across reservoir hosts. Recent research demonstrates that within bats, the order harboring many progenitors of these viruses, high viral epidemic potential clusters within specific phylogenetic clades, often composed of cosmopolitan families, rather than being evenly distributed [13]. This underscores the need for targeted surveillance of these high-priority clades.

The One Health approach—integrating human, animal, and environmental health—is paramount for mitigating spillover risk. This is vividly illustrated by the spread of avian influenza (H5N1) into dairy cows and then to humans [47], and by the underdiagnosis of globally significant zoonotic diseases like leptospirosis, which is amplified by flooding and climate factors [47]. Future research must focus on characterizing the mechanisms of viral tolerance in reservoir hosts, predicting the functional effects of viral genetic variation on transmissibility, and developing broad-spectrum countermeasures. This will require a sustained commitment to fundamental viral ecology, improved diagnostic capacity, and the development of novel experimental platforms, like the HN-NP system, that safely and effectively elucidate the dynamics of emergence.

Understanding the evolutionary drivers of viral host jumps is critical for mitigating emerging infectious diseases. A core aspect of this process is viral adaptation to new host environments, where natural selection acts on viral genomes to optimize fitness. Recent large-scale genomic studies reveal that the patterns of this adaptation are not uniform across the viral kingdom. Instead, the specific genomic targets of natural selection during host jumps vary significantly between different viral families, presenting a complex landscape of evolutionary strategies [92]. This technical guide synthesizes current research to provide an in-depth analysis of how these genomic targets of selection differ across viral taxa, framed within the critical context of predicting and preventing viral zoonosis. For researchers and drug development professionals, this knowledge is not merely academic; it pinpoints the precise genetic battlegrounds where host-pathogen interactions are negotiated, highlighting family-specific vulnerabilities that could be exploited for novel therapeutic and surveillance strategies.

The Evolutionary Drivers of Host Jumps and Genomic Adaptation

Viral host jumps, whether zoonotic (from animals to humans) or anthroponotic (from humans to animals), are catalyzed by evolutionary adaptation. Analysis of publicly available viral genomic data demonstrates that viral lineages involved in putative host jumps show clear signs of heightened evolution [92]. The extent and nature of this adaptation, however, are not random. A key finding is that the degree of adaptation associated with a host jump is inversely correlated with the virus's inherent host range. Viruses that are generalists, infecting a broad range of hosts, demonstrate a lower extent of detectable adaptation upon a new host jump compared to specialist viruses [92]. This suggests that generalist viruses may possess pre-adapted genomic features that facilitate cross-species transmission with fewer genetic modifications.

The overarching genomic analysis further reveals that the process of host jumping is not a one-way street from animals to humans. Surprisingly, humans may act as a source for viral spillover to other animals more frequently than they act as a sink, with more inferred viral host jumps from humans to other animals than from animals to humans [92]. This bidirectional exchange underscores the complexity of the global viral sharing network and emphasizes the importance of studying adaptation across all vertebrate species to fully comprehend the dynamics that impact human health.

Variation in Genomic Targets of Selection Across Viral Families

The most critical insight from recent research is that the genomic targets of natural selection associated with host jumps are not conserved; they vary fundamentally across different viral families [92]. In some viral families, selection predominantly targets genes encoding structural proteins. These proteins, which often form the viral capsid or envelope, are the primary interfaces for interaction with host cell receptors and are key to initial infection and immune system evasion. Adaptation in these genes can alter host tropism and enable escape from neutralising antibodies.

In other viral families, the prime targets of selection are auxiliary genes [92]. These genes, which are often non-essential for basic replication in vitro, typically encode proteins involved in modulating the host's immune response and cellular environment. They are crucial for in vivo pathogenicity, replication efficiency, and establishing a successful infection within a new host organism. The specific targeting of auxiliary genes highlights the importance of host-directed pathogenesis, not just cell entry, in facilitating a successful host jump.

Table 1: Genomic Targets of Selection in Different Viral Families

Viral Family Example	Primary Genomic Target of Selection	Potential Functional Consequences of Adaptation
Families targeting Structural Genes	Viral envelope (E), membrane (M), capsid (C) proteins	Altered host cell receptor binding; changed antigenicity; modified virion stability
Families targeting Auxiliary Genes	Non-structural proteins; accessory proteins (e.g., ORFs)	Enhanced immune evasion (e.g., interferon antagonism); altered viral pathogenesis; modulated host cell processes

Experimental Protocols for Identifying Family-Specific Adaptation

Identifying the genomic signatures of virus-family-specific adaptation requires a combination of robust bioinformatic pipelines and curated genomic datasets. The following protocols detail the key methodologies used in contemporary research.

Protocol 1: Large-Scale Genomic Analysis of Host Jumps

This protocol, derived from a comprehensive analysis of ~59,000 viral genomes, is designed to identify putative host jumps and quantify associated adaptation at a macro-evolutionary scale [92].

• Data Curation and Quality Control: Retrieve all available viral genomic sequences and associated metadata from public databases (e.g., NCBI Virus). Perform rigorous quality control, excluding sequences with poor quality or incomplete metadata.
• Define Species-Agnostic Taxonomic Units: To overcome inconsistencies in formal viral taxonomy, define "viral cliques" using a network theory approach. This involves clustering sequences based on genetic similarity into discrete units with comparable levels of diversity, ensuring biologically relevant and comparable groupings for downstream analysis [92].
• Identify Putative Host Jumps: For each viral clique, generate curated whole-genome alignments. Reconstruct maximum-likelihood phylogenetic trees from these alignments. Use statistical methods on the phylogeny to identify lineage diversification events that are correlated with a change in host species, inferring these as putative host jumps.
• Quantify Adaptive Evolution: Apply phylogenetic methods to the aligned sequences and tree to quantify the strength of natural selection. Common metrics include the dN/dS ratio (non-synonymous to synonymous substitutions), which indicates positive selection when >1, or more sophisticated models that detect episodic selection.
• Correlate Adaptation with Genomic Features: Map the signals of adaptation onto the genome and categorize them by gene function (e.g., structural vs. auxiliary). Correlate the extent and location of adaptation with viral traits, such as host range, to identify family-specific patterns.

Protocol 2: Machine Learning for Host Prediction Using K-mer Features

This protocol uses machine learning to predict host origin based on short genomic sequences, which can indirectly capture the adaptive k-mer signatures that are distinctive of a host environment [65].

• Dataset Construction: Compile a dataset of complete viral genome sequences from a database with validated host information (e.g., Virus-Host DB). Exclude arboviruses and segmental genomes (or concatenate them). Reduce redundancy by clustering highly identical sequences.
• Feature Extraction (K-mer Frequencies): For each viral genome, compute the normalized frequency of all possible nucleotide subsequences of length k (k-mers). For example, with k=4, there are 256 possible k-mers. The resulting feature vector represents the genomic "signature" of the virus. Amino acid k-mer frequencies from translated coding sequences can also be used [65].
• Model Training and Validation: Split the dataset into training and test sets using a stringent approach where viral genera in the test set are absent from the training set. This ensures the model generalizes to novel viruses. Train a machine learning classifier, such as a Support Vector Machine (SVM) with a linear kernel, using the k-mer frequency vectors as input features to predict the host class [65].
• Feature Importance Analysis: Analyze the trained model to identify which k-mers were most influential for accurate host prediction. These k-mers can be mapped back to genomic regions and genes, highlighting areas under host-specific selective pressure.

Diagram 1: Host Jump Genomics Workflow

Research Reagent Solutions for Viral Adaptation Studies

A successful research program in virus-family-specific adaptation relies on a suite of computational and data resources. The following table details key reagents and their applications.

Table 2: Essential Research Reagents and Resources

Research Reagent / Resource	Type	Function in Viral Adaptation Research
NCBI Virus Database	Data Repository	Primary source for obtaining viral genomic sequences and associated host metadata for analysis [92].
Virus-Host Database	Curated Data	Provides expertly curated information on virus-host associations, essential for training and validating models [65].
ICTV Metadata Resource	Taxonomy Reference	The authoritative source for official viral taxonomy, necessary for consistent classification and reporting [93].
K-mer Frequency Vectors	Computational Feature	Serves as a sequence composition signature for machine learning models to predict host origin and infer selective pressure [65].
Graph Contrastive Learning Models"	Algorithm	Advanced neural network for predicting virus-host interactions by learning from heterogeneous graph data [94].
Foundational Genomics Models"	Algorithm	Pre-trained models (e.g., DNABERT-S, HyenaDNA) that can be fine-tuned for tasks like viral read classification and detecting evolutionary signals [95].

The investigation into virus-family-specific adaptation reveals a sophisticated evolutionary landscape where the genomic targets of selection are highly dependent on viral taxonomy. The dichotomy between selection acting on structural genes versus auxiliary genes across different families provides a critical framework for understanding the molecular mechanisms underpinning host jumps and zoonotic potential. This knowledge directly informs public health surveillance by prioritizing monitoring of specific genomic regions in emerging viruses based on their family. Furthermore, for therapeutic development, it highlights that effective strategies may need to be tailored to viral taxa, targeting the specific proteins and pathways that are the primary foci of adaptive evolution. As genomic databases expand and analytical methods, from phylogenetic to machine learning approaches, become more powerful, the capacity to predict the evolutionary trajectories of emerging viruses and design countermeasures will be fundamentally enhanced by a deep appreciation of these family-specific adaptive signatures.

The persistent threat of viral zoonoses, diseases that jump from animals to humans, underscores a critical need for robust predictive models. Such models are essential for pre-empting pandemics and mitigating the profound impacts on global health and economies [47] [80]. This technical guide explores the validation of these models through the retrospective analysis of two significant viruses: SARS-CoV-2, which caused the COVID-19 pandemic, and H5N1 avian influenza, which continues to cause outbreaks in animal and human populations [96] [97]. The core thesis is that the evolutionary trajectory and spillover potential of zoonotic viruses are governed by identifiable ecological, genetic, and socio-economic drivers. By examining past emergence events, we can refine model accuracy, identify key parameters for future surveillance, and strengthen our preparedness for the next Disease X. The validation process itself is a cornerstone for building trust in predictive analytics among researchers, public health officials, and drug development professionals.

Modeling Approaches for Viral Emergence

Predictive models for infectious diseases generally fall into three categories: mathematical/statistical models, machine learning (ML)-based models, and hybrid approaches that integrate both. A recent systematic review found that of 43 studies on avian influenza, 60.5% used mathematical/statistical models, 27.9% used machine learning models, and 11.6% employed hybrid models [98]. Each category serves distinct primary purposes; mathematical models often address transmission dynamics, while ML models excel at risk assessment and outbreak prediction.

Table 1: Categorization of Modeling Approaches for Viral Emergence

Model Type	Primary Applications	Key Strengths	Common Algorithms/Techniques
Mathematical/Statistical	Transmission dynamics, Intervention evaluation [98]	Mechanistic understanding, Scenario testing	SEIR models, Compartmental models, Statistical regression
Machine Learning (ML)	Risk assessment, Outbreak prediction [98]	Handling complex, non-linear datasets, High predictive accuracy	Random Forests, XGBoost, SVM (Support Vector Machines) [96] [98]
Hybrid Models	Enhanced prediction accuracy, Understanding complex transmission [98]	Combines mechanistic and data-driven advantages	ML integrated within mechanistic frameworks

Machine learning models, in particular, have demonstrated remarkable predictive capability. A model developed for HPAI in Europe achieved an accuracy of 94% during training and 88% on a true out-of-sample test, dynamically identifying critical determinants like temperature, water index (NDWI), vegetation index (NDVI), and poultry density [96]. This highlights the power of ML to uncover complex, non-linear relationships between environmental factors and outbreak risk.

Retrospective Analysis of SARS-CoV-2 Models

The COVID-19 pandemic served as a massive, real-world test for predictive models. While the initial focus was on short-term forecasting, retrospective analyses provide invaluable insights for future pandemic preparedness. A key lesson is the potential value of broadly protective vaccines, which could have significantly altered the course of the pandemic. Modeling studies estimate that had a broadly protective sarbecovirus vaccine been available and stockpiled, as many as 65% of deaths in the first year of the COVID-19 pandemic could have been averted [99]. This finding validates models that emphasize pre-emptive, platform-based vaccine technologies as a core component of pandemic preparedness.

Retrospective validation also involves assessing the performance of outbreak models against the actual timeline of variant emergence and spread. For instance, the emergence of variants with immune-escape properties was a critical factor that many early models failed to fully account for. Current scenario modeling for COVID-19 now explicitly incorporates these factors, projecting different peak weekly hospitalization rates based on whether a variant with moderate immune-escape properties emerges (Scenario B: 6.7-9.5/100,000) or not (Scenario A: 3.8-5.9/100,000) [100]. This refined approach, informed by past data, demonstrates how model validation leads to more sophisticated and useful tools for public health decision-making.

Retrospective Analysis of H5N1 Models

H5N1 provides a compelling case for validating models against an ongoing, evolving zoonotic threat. The virus's ecology is complex, involving wild birds, domestic poultry, and an expanding range of mammalian hosts [96] [47]. Retrospective analysis of H5N1 outbreaks in Europe between 2006 and 2021 allowed researchers to train and test a high-resolution ML model. The model's high accuracy (88% on out-of-sample data) validates the importance of specific, time-varying eco-climatic drivers [96].

Table 2: Key Predictors for H5N1 Outbreaks Identified by Machine Learning

Predictor Variable	Role in Outbreak Risk	Temporal Variation
Poultry Density [96]	Increases host availability and transmission potential in domestic populations	Consistent importance
Temperature [96]	Influences virus survival and host behavior	Critical at specific times of the year
Water Index (NDWI) [96]	Determines waterbird aggregation sites; a key interface for wild-domestic transmission	Seasonal importance
Vegetation Index (NDVI) [96]	Indicator of habitat suitability for wild bird reservoirs	Seasonal importance
Infected Wild Birds [96]	Direct source of virus introduction into poultry populations	Varies with wild bird migration and epizootics

Another critical aspect of H5N1 model validation is genomic surveillance. A vast genomic analysis revealed a surprising finding: humans are as much a source as a sink for viral spillover, with more inferred viral host jumps from humans to other animals than from animals to humans [10]. This insight, which challenges conventional wisdom, is crucial for validating and refining models of viral evolution and spread. It underscores the need for models that account for multi-host transmission networks and bidirectional spillover, rather than simple linear zoonotic pathways.

Experimental Protocols for Model Validation

Data Collection and Preprocessing for Epidemiological Models

Objective: To compile and clean a high-resolution dataset of historical outbreak events and associated predictor variables for model training and testing. Materials: Outbreak data from official databases (e.g., WOAH's WAHIS [96]), eco-climatic data (e.g., from Copernicus Climate Change Service [96]), socio-economic data (e.g., from Eurostat [96]), and remote sensing data (e.g., NDVI/NDWI from Landsat/MODIS [96]). Workflow:

• Data Aggregation: Geocode all outbreak reports (longitude, latitude) to a standard administrative level (e.g., NUTS3 regions [96]).
• Data Cleaning: Remove records that cannot be accurately mapped. For temporal variables, apply corrections for hemispheric seasons (e.g., shift by 6 months for the Southern Hemisphere [101]).
• Variable Intersection: Use a geospatial modeling environment to extract the values of all predictor variables at the coordinates of each outbreak and non-outbreak data point.
• Train-Test Split: Split the data chronologically. For example, use data from 2006-2020 for training, and hold out the most recent data (e.g., 2021) for out-of-sample testing, further splitting this into validation and test sets [96].

Genomic Analysis of Viral Host Jumps

Objective: To identify putative host jumps and quantify associated adaptive evolution from viral genomic sequence data. Materials: Quality-controlled viral genomes from public databases (e.g., NCBI Virus [10]), high-performance computing resources, phylogenetic software (e.g., IQ-TREE, BEAST). Workflow:

• Viral Clique Definition: Implement a species-agnostic network analysis to group viral sequences into discrete taxonomic units ("cliques") based on genetic similarity, mitigating issues with inconsistent taxonomy [10].
• Phylogenetic Reconstruction: For each viral clique, produce curated whole-genome alignments (or single-gene alignments for segmented viruses). Reconstruct maximum-likelihood phylogenetic trees, rooting them with suitable outgroups identified via alignment-free distance metrics [10].
• Host Jump Inference: Use the phylogenetic trees to infer cross-species transmission events. A host jump is postulated when two sequences from different host species are sister taxa on the tree and their divergence is temporally consistent with a spillover event [10].
• Selection Analysis: Test for signatures of positive selection in viral lineages associated with inferred host jumps using methods like dN/dS (PAML) or genome-wide site-specific tests.

Clinical and Environmental Surveillance Assay Validation

Objective: To develop and validate a diagnostic assay for rapid detection of a specific zoonotic virus in clinical or environmental samples, supporting surveillance data quality. Materials: Clinical specimens (nasal, nasopharyngeal, conjunctival swabs), synthetic RNA templates or inactivated virus, RNA extraction kits, RT-qPCR instrumentation [97]. Workflow (Based on H5 subtyping RT-qPCR assay validation [97]):

• Assay Design: Design primers and probes targeting a conserved region of the pathogen's genome (e.g., H5 hemagglutinin).
• Limit of Detection (LoD): Determine the lowest concentration of the pathogen that can be reliably detected. Perform serial dilutions of synthetic RNA or inactivated virus in a relevant matrix. The LoD for the validated H5 assay was 250 copies/mL [97].
• Analytical Specificity: Test against a panel of other common pathogens (e.g., seasonal influenza H1N1/H3N2, other respiratory viruses) to ensure no cross-reactivity [97].
• Clinical Performance: Retrospectively and prospectively test clinical specimens that are positive for the broader pathogen group (e.g., Influenza A) to determine the prevalence of the specific subtype (e.g., H5) in the population [97].

Model Validation Workflow

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents and Materials for Viral Emergence Studies

Item	Function/Application	Example/Specification
Viral Genomic RNA	Source material for sequencing and assay development	Synthetic RNA templates (e.g., NIST H5N1 SRM 10263 [97]); Inactivated virus (e.g., from BEI Resources [97])
RT-qPCR Assay Kits	Molecular detection and subtyping of viral pathogens	Laboratory-developed tests for specific targets (e.g., H5 HA gene); FDA-approved EUA kits [97]
High-Fidelity Polymerase	Whole-genome amplification for sequencing	Kits suitable for long-range RT-PCR to generate sequencing templates for diverse viruses
Cell Lines	Virus propagation and titration	MDCK cells (influenza), Vero E6 cells (SARS-CoV-2, other viruses) [97]
Next-Generation Sequencing Platforms	Metagenomic analysis, variant detection, and genomic surveillance	Illumina, Nanopore for rapid, high-throughput pathogen characterization
SwRI Rhodium Software	Machine learning for virtual screening of antiviral compounds [14]	Identifies potential treatments for highly pathogenic viruses (e.g., Nipah, Hendra) by analyzing protein structures

The retrospective validation of predictive models against the emergence of SARS-CoV-2 and H5N1 provides a robust framework for enhancing our preparedness for future viral threats. Key takeaways include the demonstrated high accuracy of machine learning models that incorporate eco-climatic and socio-economic drivers, the critical importance of genomic surveillance in understanding viral evolution and host jumps, and the value of broadly protective countermeasures. Future efforts must focus on standardizing validation protocols across studies, improving the integration of real-time environmental and genomic data, and fostering global data-sharing initiatives. By systematically learning from past outbreaks, the scientific community can develop more reliable models that not only predict the next spillover event but also inform the development of vaccines and therapeutics, ultimately mitigating the impact of future pandemics.

This whitepaper assesses the current development and regulatory approval status of medical countermeasures for diseases identified by the World Health Organization as priority pathogens in emergency contexts. Our analysis reveals a heterogeneous landscape of preparedness: while significant progress has been achieved for certain viral threats like COVID-19 with multiple licensed vaccines and therapeutics, numerous WHO Blueprint priority pathogens—including Crimean-Congo haemorrhagic fever, Nipah virus, and the conceptual "Disease X"—lack approved human vaccines. The recent advent of prototype pathogen approaches and advanced platform technologies offers promising pathways for accelerated development against both known and unknown threats. However, substantial gaps remain in our readiness for the next potential pandemic, necessitating reinforced commitment to vaccine and therapeutic development for priority pathogens with epidemic potential.

The World Health Organization's Research and Development Blueprint represents a global strategy and preparedness plan to accelerate research and development for epidemics and pandemics. This initiative recognizes that while the number of potential pathogens is vast, resources for disease R&D are limited, necessitating careful prioritization [102]. The Blueprint focuses on diseases and pathogens that pose substantial public health risk due to their epidemic potential and for which insufficient or no medical countermeasures exist [102].

A fundamental concept within the Blueprint is "Disease X," representing the knowledge that a serious international epidemic could be caused by a pathogen currently unknown to cause human disease [102]. This conceptual category drives the development of platform technologies and cross-cutting preparedness approaches that can be rapidly adapted when novel threats emerge.

The July 2024 updated WHO list of emerging pathogens signifies an evolution in global approach, shifting focus from specific pathogens to adopting a broader family-focused approach and incorporating 'Prototype Pathogens' and 'Pathogen X' into its risk classification [75]. This framework aims to foster a more proactive, flexible strategy for addressing both familiar and unfamiliar pandemic risks.

Current WHO Priority Pathogens and Medical Countermeasure Status

WHO Blueprint Priority Diseases Landscape

The current WHO priority diseases list represents pathogens with significant epidemic potential that warrant focused R&D efforts [102]:

• COVID-19
• Crimean-Congo haemorrhagic fever
• Ebola virus disease and Marburg virus disease
• Lassa fever
• Middle East respiratory syndrome coronavirus (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS)
• Nipah and henipaviral diseases
• Rift Valley fever
• Zika
• "Disease X"*

*Disease X represents the knowledge that a serious international epidemic could be caused by a pathogen currently unknown to cause human disease [102].

This list is dynamically reviewed and updated as methodologies evolve and new threats emerge. It serves to guide the development of targeted R&D roadmaps for each disease, coordinating global efforts to address the most pressing threats to global health security.

Licensed Vaccines and Therapeutics for Priority Pathogens

Table 1: Current Licensing Status of Medical Countermeasures for WHO Blueprint Priority Diseases

Pathogen/Disease	Vaccine Status (Human Use)	Therapeutic Status	Key Developments
COVID-19 (SARS-CoV-2)	Multiple FDA-approved vaccines including MNEXSPIKE (mRNA) and NUVAXOVID (adjuvanted) [103]	WHO-developed clinical practice guidelines for therapeutics; multiple under ongoing assessment (SGLT2, heparin, VV116, simvastatin, metformin) [104]	Regular updates to clinical guidelines based on emerging evidence [104]
Chikungunya	VIMKUNYA FDA-approved February 2025 (recombinant vaccine for individuals ≥12 years) [103]	Limited specific therapeutics	First vaccine approval represents significant milestone
Ebola virus disease	Vaccine available (rVSV-ZEBOV licensed in 2019)	Limited therapeutic options	Priorities include improving accessibility
Nipah virus	No licensed human vaccine	No specific antivirals	100,000-dose investigational vaccine reserve created by Serum Institute of India and CEPI for Phase II trials and emergency use [105]
Zika virus	No licensed vaccine	No specific antivirals	Several candidates in preclinical and early clinical development
Rift Valley fever	No licensed human vaccine	No specific therapeutics
Lassa fever	No licensed vaccine	Limited therapeutic options (ribavirin used off-label)
Crimean-Congo haemorrhagic fever	No licensed vaccine	Limited evidence-based therapeutics
MERS-CoV	No licensed vaccine	Supportive care primarily

Table 2: Recent FDA Vaccine Approvals (2025) Relevant to Epidemic Preparedness

Vaccine Name	Type	Indication	Approval Date
VIMKUNYA	Chikungunya Vaccine, Recombinant	Prevention of disease caused by chikungunya virus in individuals ≥12 years	February 14, 2025 [103]
MNEXSPIKE	COVID-19 Vaccine, mRNA	Prevention of COVID-19 in high-risk individuals and those ≥65 years	May 30, 2025 [103]
NUVAXOVID	COVID-19 Vaccine, Adjuvanted	Prevention of COVID-19 in adults ≥65 years and high-risk individuals 12-64 years	May 16, 2025 [103]
PENMENVY	Meningococcal Groups A, B, C, W, and Y Vaccine	Prevention of invasive meningococcal disease in individuals 10-25 years	February 14, 2025 [103]

Methodological Framework for Assessing Zoonotic Potential and Vaccine Development

Experimental Approaches for Evaluating Viral Zoonotic Potential

Pathogen-Host Interaction Studies

• Surface Protein Characterization: Utilize surface plasmon resonance and cryo-electron microscopy to analyze pathogen attachment to human cellular receptors
• Cell Culture Models: Employ primary human airway epithelial cultures, organoid systems, and humanized mouse models to assess infectivity and replication efficiency
• Inter-species Transmission Studies: Conduct in vitro adaptation experiments to identify mutations that enhance cross-species transmission capability

Immune Response Profiling

• Humoral Immunity Assessment: Perform microneutralization assays, ELISA, and plaque reduction neutralization tests to quantify neutralizing antibody responses
• Cellular Immunity Evaluation: Utilize IFN-γ ELISpot, intracellular cytokine staining, and MHC multimer staining to characterize T-cell responses
• Immune Evasion Mechanisms: Investigate viral interferon antagonist activity and capacity to overcome host restriction factors

Animal Challenge Models

• Route of Infection Studies: Compare disease pathogenesis following various inoculation routes (intranasal, intraperitoneal, subcutaneous) relevant to natural transmission
• Vaccine Efficacy Testing: Conduct challenge experiments in appropriate animal models (including non-human primates) at biosafety level 3 or 4 facilities
• Transmission Chain Analysis: Implement sequential passage experiments to assess potential for sustained transmission

Vaccine Platform Assessment Methodologies

Platform Technology Evaluation

• mRNA Vaccine Platforms: Assess stability, immunogenicity, and manufacturing scalability of lipid nanoparticle-formulated mRNA constructs
• Viral Vector Platforms: Characterize pre-existing immunity, durability of response, and manufacturing considerations for adenovirus, VSV, and other viral vectors
• Protein Subunit Platforms: Evaluate antigen design, adjuvant selection, and production scalability for recombinant protein approaches
• Whole Inactivated/Virus-like Particle Platforms: Assess preservation of conformational epitopes, safety profile, and production yield

Cross-Protection Assessment

• Sequence Conservation Analysis: Employ bioinformatic approaches to identify conserved epitopes across viral variants and related strains
• Heterologous Challenge Studies: Evaluate protection against diverse strains within pathogen families in animal models
• Immune Breadth Characterization: Map antibody and T-cell responses to multiple variants using multiplexed assays

Research Reagent Solutions for Zoonotic Virus Research

Table 3: Essential Research Reagents for Priority Pathogen Investigation

Reagent Category	Specific Examples	Research Application	Key Considerations
Cell Line Models	Vero E6, Calu-3, Huh-7, primary human airway epithelial cultures	Viral replication studies, tropism assessment, antiviral screening	Species origin, relevant receptor expression, interferon competence
Animal Models	Humanized ACE2 mice, ferrets, non-human primates	Pathogenesis studies, transmission evaluation, vaccine efficacy testing	Species susceptibility, clinical disease recapitulation, biosafety requirements
Immunological Assays	Pseudovirus neutralization, ELISpot, intracellular cytokine staining	Immune response characterization, correlates of protection determination	Assay validation, biological relevance, standardization across labs
Molecular Tools	Reverse genetics systems, recombinant viral constructs	Viral protein function studies, vaccine vector development, mutagenesis analysis	Genetic stability, replication competence, safety considerations
Protein Reagents	Recombinant viral proteins, monoclonal antibodies	Structural studies, serological assay development, therapeutic candidate screening	Conformational integrity, post-translational modifications, batch consistency
Diagnostic Components	Polyclonal antisera, reference standards, positive controls	Assay development, validation, and standardization	Specificity, sensitivity, availability, regulatory status

Analysis of Preparedness Gaps and Research Priorities

Critical Gaps in Medical Countermeasure Coverage

Our analysis identifies significant disparities in preparedness across the WHO priority pathogens landscape. While coronaviruses have received substantial attention and resource allocation following the COVID-19 pandemic, several other priority pathogens remain neglected in vaccine development pipelines.

The filovirus family (Ebola, Marburg) demonstrates both progress and persistent challenges. The licensing of rVSV-ZEBOV following the 2014-2016 West African Ebola outbreak represented a major achievement in rapid-response vaccine development [48]. However, accessibility and implementation challenges remain, particularly in resource-limited settings where outbreaks typically occur. For Marburg virus, no licensed vaccines or specific therapeutics are yet available despite its inclusion on the priority list since the Blueprint's inception.

The paramyxovirus family (Nipah, Hendra) presents particular concerns due to their high case fatality rates and capacity for human-to-human transmission. The recent creation of a 100,000-dose Nipah virus vaccine reserve by the Serum Institute of India in collaboration with CEPI and Oxford University represents a promising development for outbreak response capabilities [105]. This "just-in-case" stockpile approach may provide a model for other priority pathogens with epidemic potential.

For vector-borne viral diseases (Rift Valley fever, Crimean-Congo hemorrhagic fever, Zika), vaccine development faces additional complexities of ecology, changing transmission patterns due to climate change, and heterogeneous risk distribution. The recent approval of a chikungunya vaccine (VIMKUNYA) in February 2025 demonstrates that progress is possible for arboviral diseases, potentially creating a pathway for other vector-borne priority pathogens [103].

The Challenge of "Disease X" and Prototype Pathogen Approaches

The "Disease X" concept acknowledges that the next pandemic may be caused by a pathogen currently unknown to cause human disease [102]. This recognition has catalyzed a strategic shift toward platform technologies and prototype pathogen approaches.

Viral prospecting - the systematic sampling of animals to detect novel viruses before they infect humans - has been proposed as a strategy for pandemic preparedness. However, recent analyses question whether viral discovery in animal hosts meaningfully accelerates medical countermeasure development [48]. Examination of historical patterns reveals that most major 21st-century outbreaks were caused by viruses already known to infect humans before 2000, and there is limited evidence that viral prospecting has accelerated vaccine or therapeutic development [48].

This analysis suggests that alternative preparedness strategies may offer more efficient pathways to readiness:

• Prototype pathogen approach: Focusing development efforts on representative viruses within families of epidemic potential (e.g., coronaviruses, filoviruses, paramyxoviruses)
• Platform technology investment: Advancing modular vaccine platforms (mRNA, viral vectors) that can be rapidly adapted when novel threats emerge
• Pathogen-agnostic technologies: Developing broad-spectrum antivirals and host-directed therapies effective across multiple viral families

The licensing status of vaccines and therapeutics for WHO Blueprint priority diseases reflects both significant achievements and concerning vulnerabilities in global pandemic preparedness. The rapid development and deployment of COVID-19 vaccines demonstrated the potential of modern platform technologies, while the continued absence of licensed medical countermeasures for numerous priority pathogens highlights persistent systemic gaps.

The evolving threat landscape, characterized by climate change, ecosystem disruption, and increasing human-animal interfaces, necessitates reinforced commitment to priority pathogen R&D. Promising developments include the application of prototype pathogen approaches, advances in platform technologies, and innovative financing mechanisms for pipeline candidates that may never see traditional commercial markets.

For the research community, strategic priorities should include: (1) accelerating development for pathogens with the greatest gaps in medical countermeasures, particularly Nipah virus, Crimean-Congo haemorrhagic fever, and other high-case-fatality threats; (2) advancing platform technologies that enable rapid response to unknown threats; and (3) strengthening the evidence base for therapeutic interventions across the priority pathogen landscape.

Bridging these preparedness gaps will require sustained collaboration across academic, industry, government, and non-profit sectors, with aligned incentives and shared commitment to global health security. The WHO R&D Blueprint provides the essential framework for these efforts, but its success depends on continued investment and scientific innovation to address both known priorities and the unknown threat of Disease X.

Conclusion

The critical synthesis of evolutionary biology, genomics, and ecology is fundamentally advancing our ability to understand and predict viral zoonotic potential. While methodological breakthroughs in machine learning and genomic surveillance offer promising paths for proactive risk assessment, their effectiveness is hampered by persistent surveillance gaps, data quality issues, and a scarce therapeutic arsenal for known threats. Future efforts must prioritize closing these data gaps through equitable global sequencing initiatives, rigorously validating predictive models with laboratory studies, and accelerating the development of broad-spectrum countermeasures. Embracing a truly integrated One Health approach is not merely beneficial but essential for strengthening our collective defense against the inevitable future emergence of zoonotic pathogens with pandemic potential.

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