Targeting Zoonotic Spillover Drivers for Global Pandemic Prevention: A Narrative Review
Abstract
1. Introduction
2. Historical and Evolutionary Drivers of Zoonotic Viral Spillover
3. Stages of Viral Adaptation and Zoonotic Transmission
4. Mechanisms Driving the Transition from Spillover to Pandemic
4.1. Ecological Framework and Wildlife Reservoirs of Zoonoses
4.2. Pathogen-Specific Determinants of Adaptive Transmission
4.3. Host-Specific Susceptibility and Immunological Determinants
4.4. Anthropogenic Drivers and Commercial Wildlife Interfaces
4.5. Environmental Encroachment and Climate-Driven Vector Dynamics
5. Pandemic Prevention Frameworks and One Health Surveillance
5.1. Mitigation Strategies for Wildlife-Associated Zoonoses
5.2. Control Methodologies for Vector-Borne Pathogens
6. Knowledge Gaps and Future Directions
Structured Operational Actions
- Identify Receptor Co-evolution: Fund deep-sequencing and structural biology projects focusing on how viral surface glycoproteins bind to animal vs. human cellular receptors [194].
- Profile Tissue Tropism: Map downstream intracellular restrictions (such as host polymerase inhibitors or innate immune system evasion pathways) that limit replication in human cells.
- Target Biodiversity Hotspots: Direct molecular screening teams explicitly to primary tropical forest borders, land-use change zones, and active agricultural frontiers [195].
- Build Non-invasive Bio-banks: Systematically archive fecal, saliva, and environmental DNA (eDNA) samples from bats, rodents, and wild non-human primates to uncover hidden viral diversity without harming native ecosystems [196].
- Monitor High-Risk Interfaces: Establish ongoing, multi-year medical monitoring of forest workers, miners, livestock handlers, and wildlife trade market vendors [197].
- Utilize Multiplex Serology: Run regular pan-viral serological panels on these cohorts to catch unrecognized, mild, or self-limiting spillover events before human-to-human transmission evolves [198].
- Merge Multi-Sector Data: Create computational platforms that combine ecological tracking data (deforestation rates, wildlife migration patterns, climate anomalies) with veterinary diagnostics and human hospital admission feeds [199].
- Co-locate Sampling Networks: Ensure that when a wildlife disease cluster or unexpected die-off is investigated, local livestock and adjacent human communities are simultaneously sampled.
- Enact Open-Access Frameworks: Build globally accessible, secure digital sequence repositories that guarantee immediate, unhindered sharing of genetic data during suspected outbreaks [199].
- Harmonize Metadata Standards: Enforce universal protocols for describing sample locations, host species, environmental metadata, and clinical definitions across different nations.
- Incentivize Sovereign Cloud/Lab Infrastructure: Provide direct financial and technical investments to build advanced genomics labs, training facilities, and localized data hubs directly inside resource-limited, high-risk regions.
- Reform Benefit-Sharing Mechanisms: Guarantee that the global South nations providing raw viral surveillance data receive early, equitable, and low-cost access to any resulting diagnostics, vaccines, or therapeutics [200].
- Formulate Measurable Indicators: Convert abstract ecological risk models into clear, actionable metrics (e.g., specific boundaries on wildlife trading or thresholds for deforestation) that lawmakers can legally enforce.
- Mandate Transdisciplinary Advisory Panels: Embed permanent One Health scientists directly into national security, environmental protection, and financial planning ministries rather than leaving them isolated inside public health agencies [201].
7. Conclusions
- Targeted Surveillance: Deploy continuous genetic sequencing at high-risk animal–human interfaces, such as live animal markets and deforestation frontiers, to detect early pathogen mutations.
- Integrated Data Sharing: Establish unified, real-time global databases that link veterinary, environmental, and human health data to identify spillover flags before widespread transmission occurs.
- Infrastructure Investment: Strengthen local healthcare infrastructure in spillover hot spots to ensure rapid diagnostics, immediate isolation capabilities, and swift decentralized containment responses.
- One Health Frameworks: Institutionalize collaborative policies across environmental, agricultural, and public health sectors to regulate wildlife trade and preserve natural habitats, directly reducing human–animal exposure.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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| Virus | Reservoir | Intermediate Host | Vector | Transmission to Humans | Ref |
|---|---|---|---|---|---|
| Marburg | Fruit bats | Non-human primates (amplifying) | None | Contact with wildlife/fluids | [36] |
| Ebola | Fruit bats | Non-human primates (amplifying) | None | Contact with wildlife/fluids | [37] |
| SIV | Old world primates | None | None | Wildlife handling and consumption | [38] |
| Hepatitis E | Domestic pigs and wild boars | None | None | Fecal-oral route, pork consumption | [39] |
| Swine influenza viruses (Type A, subtypes H1N1, H1N2, H3N2) | Pigs | None | None | Direct contact (pig farmers and pork producers) | [40] |
| Rota viruses G3, G5 and G9 (Group A Rotavirus) | Pigs | None | None | Fecal-oral, contaminated soil and water | [41] |
| Hendra | Fruit bats | Horses (amplifying) | None | Contact with infected horse fluids | [42] |
| Nipah | Fruit bats | Pigs (amplifying) | None | Contact with infected pig/bat fluids or contaminated food | [43] |
| Hantaviruses | Rodents | None identified | None | Direct contact or inhalation of aerosolized rodent secretions | [44] |
| Lassa | Rodents (Mastomys) | None identified | None | Direct contact or inhalation of aerosolized rodent secretions | [45] |
| Mpox | Small mammals (e.g., squirrels, rodents) | None required | None | Contact with infected animal lesions, body fluids or carcasses. | [46] |
| Rift Valley fever | Mosquitoes | Domestic ruminants | Primarily Aedes mosquitoes | Vector-borne and handling livestock tissue | [47] |
| West Nile | Birds (primarily passerine/songbirds) | None (mammals dead-end) | Culex mosquitoes | Vector-borne | [48] |
| Zika | Humans/Primates | None | Aedes mosquitoes (Aedes aegypti and Aedes albopictus) | Vector-borne | [49] |
| Yellow fever | All primates | None | Primarily Aedes mosquitoes | Vector-borne | [50] |
| Dengue | Humans/Primates | None | Aedes mosquitoes (Aedes aegypti and Aedes albopictus) | Vector-borne | [51] |
| Chikungunya | Humans, other primates, rodents, birds | None | Aedes mosquitoes (Aedes aegypti and Aedes albopictus) | Vector-borne | [52] |
| Ross River | Marsupials | None | Over 40 species of mosquitoes (mainly Aedes and Culex species) | Vector-borne | [53] |
| Japanese encephalitis | Wading birds (herons, egrets) | Pigs are the most important amplifying hosts | Culex mosquitoes | Vector-borne | [54] |
| MERS-CoV | Bats | Dromedary camels | None | Direct contact or consuming of raw/unpasteurized camel products | [55] |
| SARS-CoV-1 | Bats | Masked palm civets, raccoon dogs | None | Direct contact with infected animals or their secretions | [56] |
| SARS-CoV-2 | Bats (likely) | Unverified (pangolins and civets remain unconfirmed hypotheses) | None | Direct contact with infected animals or their secretions | [57] |
| Influenza A, subtype H5N1 | Wild waterfowl (ducks, geese, shorebirds) | Domestic poultry/Dairy cattle (act as highly susceptible spillover hosts) | None | Direct contact with sick/dead birds, infected livestock or contaminated environments (feces, litter) | [58] |
| Pandemic | Virus | Years | Estimated Deaths | Ref. |
|---|---|---|---|---|
| “Influenza” pandemic | Unknown (The influenza virus was identified in 1930s) | 1510 | Unknown (Death registration began in 1837) | [70] |
| “Influenza” pandemic | Unknown (The influenza virus was identified in 1930s) | 1557–1559 | Unknown (Death registration began in 1837) | [71] |
| “Influenza” pandemic | Unknown (The influenza virus was identified in 1930s) | 1580 | Unknown (Death registration began in 1837) | [71] |
| “Influenza” pandemic | Unknown (The influenza virus was identified in 1930s) | 1729–1733 | Unknown (Death registration began in 1837) | [71] |
| “Influenza” pandemic | Unknown (The influenza virus was identified in 1930s) | 1761 | Unknown (Death registration began in 1837) | [71] |
| “Influenza” pandemic | Unknown (The influenza virus was identified in 1930s) | 1775 | Unknown (Death registration began in 1837) | [72] |
| “Influenza” pandemic | Unknown (The influenza virus was identified in 1930s) | 1781–1782 | Unknown (Death registration began in 1837) | [71] |
| “Influenza” pandemic | Unknown (The influenza virus was identified in 1930s) | 1830–1833 | Unknown (Death registration began in 1837) | [73] |
| “Influenza” pandemic | Unknown (The influenza virus was identified in 1930s) | 1847–1851 | Unknown | [74] |
| “Russian flu” | Unknown (The influenza virus was identified in 1930s) | 1889–1890 | ~1 million | [75] |
| “Spanish flu” | Influenza A virus, subtype H1N1 | 1918–1919 | >50 million | [76] |
| “Asian flu” | Influenza A virus, subtype H2N2 | 1957–1958 | ~1–2 million | [76] |
| “Hong Kong flu” | Influenza A virus, subtype H3N2 | 1968–1969 | ~¾ million | [77] |
| “Russian flu” | Influenza A virus, subtype H1N1 | 1977–1979 | ~700.000 | [78] |
| HIV/AIDS pandemic | HIV | 1981–present | ~45 million (until 2026) | [79] |
| “Swine flu” | Influenza A virus, subtype H1N1 | 2009–2010 | Laboratory-confirmed deaths: 18.449 officially reported to the WHO by mid-2010 * | [77] |
| COVID-19 pandemic | COVID-19 | 2019–present | ~7.11 million confirmed deaths (until 2026) ** | [80] |
| Phase | Pathogen Factors | Host Factors | Human Activities |
|---|---|---|---|
| 1. Initial Cross-Species Jump |
|
|
|
| 2. Within-Host Adaptation |
| ||
| 3. Human-to-Human Spread |
|
|
|
| Interface | Key Stakeholders/Target Areas | Key Interventions | Major Challenges |
|---|---|---|---|
| Occupational | Hunters, Foresters, Zoo Workers | Targeted safety training. Mandatory vaccinations (e.g., rabies). Provision of personal protective equipment (PPE) (gloves and masks). | Poverty, lack of clean water, and deeply ingrained cultural livelihood dependencies frequently limit compliance. |
| Local Market | Traders, Consumers, Domestic Animals | Strict market hygiene rules. Physical separation of diverse species. Consumer education on thorough cooking processes. | Standard hygiene guidelines fail without strict enforcement. Economic pressures in low-income regions bypass rules. Species separation is difficult to sustain without top-down market restructure. |
| Global Trade | Importers, Exporters, Border Control | Airport and border screening. Real-time data sharing and regulation alignment. Intergovernmental surveillance body funded by high-income countries. | High-income importing nations drive macroeconomic dependencies. Severe lack of global equity in funding and resource distribution. Import bans risk pushing the wildlife trade underground rather than mitigating it. |
| Ecological and Urban-Agricultural | Urban Planners, Farmers, Displaced Wildlife | Urban buffers and infrastructure design. Strict land-use zoning against forest encroachment. Livestock biosecurity barriers. | Deforestation and fragmented landscapes create dangerous “edge effects”. Interventions require massive, cross-sectoral One Health cooperation. Commercial pressure from agriculture, logging, and development limits success. |
| Interface | Focus and Target | Interventions |
|---|---|---|
| Environmental and Sylvatic Interface (Source Control) | Preventing mosquito amplification in natural and semi-natural habitats before they interact with human populations | Biological control: Bacillus thuringiensis, Wolbachia. Environmental management: Larval source reduction (eliminating stagnant water) and habitat elimination (removing containers, puddles, and water cavities around residential areas). |
| Peri-Domestic and Agricultural Interface (Buffer Zones) | Targeting transition zones where invasive vectors breed in man-made environments and interact with livestock, working humans, and residential borders. | Larval source reduction and container management. Insect growth regulators (IGRs). |
| Domestic and Urban Interface (Direct Human Transmission) | Securing the immediate living space where the probability of vector-to-human contact and pathogen spillover is highest. | Vector control: Chemical control (Insecticide application, Pyrathroids, IGRs) and biological control (Bacillus thuringiensis, Wolbachia). Personal and community measures: Personal protective measures (Bed nets, repellents, long-sleeved clothing) and community education. |
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© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Share and Cite
Mpakosi, A.; Cholevas, V.; Lianou, A.; Tziraki, F.; Vogiatzis, I.; Cholevas, S.; Tzouvelekis, I.; Mironidou-Tzouveleki, M.; Tsante, K.A.; Tsakri, D.; et al. Targeting Zoonotic Spillover Drivers for Global Pandemic Prevention: A Narrative Review. Microorganisms 2026, 14, 1316. https://doi.org/10.3390/microorganisms14061316
Mpakosi A, Cholevas V, Lianou A, Tziraki F, Vogiatzis I, Cholevas S, Tzouvelekis I, Mironidou-Tzouveleki M, Tsante KA, Tsakri D, et al. Targeting Zoonotic Spillover Drivers for Global Pandemic Prevention: A Narrative Review. Microorganisms. 2026; 14(6):1316. https://doi.org/10.3390/microorganisms14061316
Chicago/Turabian StyleMpakosi, Alexandra, Vasileios Cholevas, Alexandra Lianou, Foteini Tziraki, Ioannis Vogiatzis, Stamatios Cholevas, Ioannis Tzouvelekis, Maria Mironidou-Tzouveleki, Konstantina A. Tsante, Deny Tsakri, and et al. 2026. "Targeting Zoonotic Spillover Drivers for Global Pandemic Prevention: A Narrative Review" Microorganisms 14, no. 6: 1316. https://doi.org/10.3390/microorganisms14061316
APA StyleMpakosi, A., Cholevas, V., Lianou, A., Tziraki, F., Vogiatzis, I., Cholevas, S., Tzouvelekis, I., Mironidou-Tzouveleki, M., Tsante, K. A., Tsakri, D., Petrakis, V., Ioannou, P., Bonovas, S., Sokou, R., & Tsantes, A. G. (2026). Targeting Zoonotic Spillover Drivers for Global Pandemic Prevention: A Narrative Review. Microorganisms, 14(6), 1316. https://doi.org/10.3390/microorganisms14061316

