Analyze Diet
Equine veterinary journal2025; 58(2); 549-563; doi: 10.1111/evj.70118

Bridging hosts: Domestic horse density and Hendra virus spillover risk in a changing landscape.

Abstract: Anthropogenic climatic and landscape change can drive behavioural shifts in wildlife and thus lead to increased risk of pathogen exposure for humans and domestic animals. While spillover research often focuses on the reservoir hosts or ongoing transmission in humans, livestock and companion animals can play important roles as bridging and amplifying hosts, facilitating the emergence of highly pathogenic diseases. Objective: To investigate the distribution and density of domestic horses in the context of their role as bridge hosts for Hendra virus and build models to study zoonotic emergence. Methods: Cross sectional. Methods: Government horse datasets (2011-2024) were analysed, and field surveys conducted in southeast Queensland and northeast New South Wales, Australia, to estimate domestic horse distributions and density. Zero-inflated negative binomial models were used to examine spatial correlations between horse population density, flying fox foraging areas and Hendra virus spillover events across different landcover types. Finally, random forest models were used to predict property-level horse densities based on 209 landscape and socioeconomic covariates. Results: Horse populations were widespread across the study area, though field observations confirmed under-reporting in government datasets. Property size was the strongest predictor of horse density. A positive relationship in agricultural areas between Hendra virus spillover events and both locality-level horse density (p = 0.001) and cumulative winter occupancy of flying fox roosts (p < 0.001) was identified. These relationships were specific to agricultural landscapes, with negative associations in urban and forested areas. Conclusions: A previously undetected association between horse density and spillover was revealed, highlighting the importance of this integrated approach. Current limitations in horse population data present challenges for biosecurity and disease risk assessments in existing risk areas. Targeted surveillance and predictive modelling will be essential to mitigate future spillover risks and protect both animal and human health.
Publication Date: 2025-11-14 PubMed ID: 41235837DOI: 10.1111/evj.70118Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This study investigates how the density of domestic horses acts as a bridge host influencing the risk of Hendra virus spillover in changing landscapes of southeastern Australia.
  • The research combines government datasets, field surveys, and statistical modeling to understand the spatial relationships between horse populations, flying fox activity, and Hendra virus spillover events.

Background and Importance

  • Anthropogenic changes such as climate and landscape modification can alter wildlife behavior, potentially increasing the exposure risk of pathogens to humans and domestic animals.
  • Hendra virus is a zoonotic pathogen transmitted from flying foxes (the natural reservoir) to horses, which then can transmit it to humans.
  • Domestic horses act as bridging hosts, playing a critical role in pathogen transmission from wildlife reservoirs to humans.
  • Understanding the distribution and density of horses helps to model and mitigate spillover risks, which is crucial for biosecurity and public health.

Research Objectives

  • Investigate the distribution and density of domestic horses in southeastern Queensland and northeast New South Wales, Australia.
  • Assess the role of horses as bridge hosts in Hendra virus spillover events.
  • Build statistical models to explore spatial correlations between horse density, flying fox foraging activity, and spillover occurrences.
  • Develop predictive models to estimate horse densities on properties using various landscape and socioeconomic factors.

Methods

  • Data sources: Government horse population datasets from 2011-2024 and field surveys conducted in the study regions.
  • Statistical modeling:
    • Zero-inflated negative binomial (ZINB) models to handle count data with excess zeros, used to examine spatial relationships between horse densities, flying fox foraging areas, landcover types, and spillover events.
    • Random forest models to predict horse density at the property level based on 209 landscape and socioeconomic variables.
  • Field Observations: Supplemented government data, addressing under-reporting issues.

Key Findings

  • Domestic horse populations are widespread across the study area, though government datasets underestimate actual numbers.
  • Property size is the strongest predictor of horse density, i.e., larger properties tend to have more horses.
  • In agricultural landscapes:
    • A significant positive correlation exists between horse density and Hendra virus spillover events (p = 0.001).
    • Cumulative winter occupancy of flying fox roosts also shows a strong positive association with spillover risk (p < 0.001).
  • In contrast, urban and forested landscapes show negative associations between horse density and spillover events, indicating landscape-specific dynamics.
  • The integrated modeling approach reveals associations previously undetected in past research.

Implications and Conclusions

  • Horse density is a critical, previously underappreciated factor influencing Hendra virus spillover risk, especially in agricultural landscapes.
  • Under-reporting and limitations in available horse population data impede accurate biosecurity assessments and disease risk forecasting.
  • Targeted surveillance efforts and enhanced predictive models are essential for:
    • Monitoring horse populations more accurately.
    • Identifying high-risk spillover areas.
    • Developing interventions to prevent virus transmission to horses and humans.
  • The study highlights the value of combining ecological, epidemiological, and socioeconomic data to understand and mitigate zoonotic disease emergence.

Cite This Article

APA
Linnegar B, Hoegh A, McCallum H, Peel AJ. (2025). Bridging hosts: Domestic horse density and Hendra virus spillover risk in a changing landscape. Equine Vet J, 58(2), 549-563. https://doi.org/10.1111/evj.70118

Publication

ISSN: 2042-3306
NlmUniqueID: 0173320
Country: United States
Language: English
Volume: 58
Issue: 2
Pages: 549-563

Researcher Affiliations

Linnegar, Belinda
  • School of Environment & Science, Griffith University, Brisbane, Queensland, Australia.
Hoegh, Andrew
  • Department of Mathematical Sciences, Montana State University, Bozeman, Montana, USA.
McCallum, Hamish
  • School of Environment & Science, Griffith University, Brisbane, Queensland, Australia.
Peel, Alison J
  • School of Environment & Science, Griffith University, Brisbane, Queensland, Australia.
  • Sydney School of Veterinary Science, Sydney University, Sydney, New South Wales, Australia.

MeSH Terms

  • Animals
  • Horses
  • Henipavirus Infections / veterinary
  • Henipavirus Infections / epidemiology
  • Henipavirus Infections / virology
  • Henipavirus Infections / transmission
  • Hendra Virus / physiology
  • Horse Diseases / virology
  • Horse Diseases / epidemiology
  • Horse Diseases / transmission
  • Queensland / epidemiology
  • Population Density
  • New South Wales / epidemiology
  • Cross-Sectional Studies
  • Risk Factors

Grant Funding

  • Australian Government Research Training Program Scholarship
  • EF-2133763 / National Science Foundation
  • EF-2231624 / National Science Foundation
  • DE190100710 / Australian Research Council

References

This article includes 86 references
  1. Allen T, Murray KA, Zambrana‐Torrelio C, Morse SS, Rondinini C, Di Marco M. Global hotspots and correlates of emerging zoonotic diseases. Nat Comm 2017;8(1):1124.
  2. Bernstein AS, Ando AW, Loch‐Temzelides T, Vale MM, Li BV, Li H. The costs and benefits of primary prevention of zoonotic pandemics. Sci Adv 2022;8(5):eabl4183.
    doi: 10.1126/sciadv.abl4183google scholar: lookup
  3. Carlson CJ, Brookson CB, Becker DJ, Cummings CA, Gibb R, Halliday FW. Pathogens and planetary change. Nat Rev Biodivers 2025;1(1):32–49.
  4. Dobson AP, Pimm SL, Hannah L, Kaufman L, Ahumada JA, Ando AW. Ecology and economics for pandemic prevention. Science 2020;369(6502):379–381.
    doi: 10.1126/science.abc3189google scholar: lookup
  5. Carroll D, Daszak P, Wolfe ND, Gao GF, Morel CM, Morzaria S. The global Virome project. Science 2018;359(6378):872–874.
    doi: 10.1126/science.aap7463google scholar: lookup
  6. Plowright RK, Parrish CR, McCallum H, Hudson PJ, Ko AI, Graham AL. Pathways to zoonotic spillover. Nat Rev Microbiol 2017;15(8):502–510.
    doi: 10.1038/nrmicro.2017.45google scholar: lookup
  7. Plowright RK, Ahmed AN, Coulson T, Crowther TW, Ejotre I, Faust CL. Ecological countermeasures to prevent pathogen spillover and subsequent pandemics. Nat Commun 2024;15(1):2577.
  8. Esposito MM, Turku S, Lehrfield L, Shoman A. The impact of human activities on zoonotic infection transmissions. Animals 2023;13(10):1646.
    doi: 10.3390/ani13101646google scholar: lookup
  9. Faust CL, McCallum HI, Bloomfield LSP, Gottdenker NL, Gillespie TR, Torney CJ. Pathogen spillover during land conversion. Ecol Lett 2018;21(4):471–483.
    doi: 10.1111/ele.12904google scholar: lookup
  10. Filion A, Deschamps L, Niebuhr CN, Poulin R. Anthropogenic landscape alteration promotes higher disease risk in wild New Zealand avian communities. PLoS One 2022;17(3):e0265568.
  11. Rulli MC, D'Odorico P, Galli N, Hayman DTS. Land‐use change and the livestock revolution increase the risk of zoonotic coronavirus transmission from rhinolophid bats. Nat Food 2021;2(6):409–416.
  12. Plowright RK, Peel AJ, Streicker DG, Gilbert AT, McCallum H, Wood J. Transmission or within‐host dynamics driving pulses of zoonotic viruses in reservoir‐host populations. PLoS Negl Trop Dis 2016;10(8):e0004796.
  13. Vinson JE, Gottdenker NL, Chaves LF, Kaul RB, Kramer AM, Drake JM. Land reversion and zoonotic spillover risk. R Soc Open Sci 2022;9(6):220582.
    doi: 10.1098/rsos.220582google scholar: lookup
  14. Hassell JM, Begon M, Ward MJ, Fevre EM. Urbanization and disease emergence: dynamics at the wildlife–livestock–human interface. Trends Ecol Evol 2017;32(1):55–67.
  15. Desvars‐Larrive A, Vogl AE, Puspitarani GA, Yang L, Joachim A, Käsbohrer A. A One Health framework for exploring zoonotic interactions demonstrated through a case study. Nat Commun 2024;15(1):5650.
  16. Morand S, McIntyre KM, Baylis M. Domesticated animals and human infectious diseases of zoonotic origins: domestication time matters.. Infect Genet Evol 2014;24:76–81.
  17. Guth S, Visher E, Boots M, Brook CE. Host phylogenetic distance drives trends in virus virulence and transmissibility across the animal–human interface.. Philos Trans R Soc Lond B Biol Sci 2019;374(1782):20190296.
    doi: 10.1098/rstb.2019.0296google scholar: lookup
  18. Jones BA, Grace D, Kock R, Alonso S, Rushton J, Said MY. Zoonosis emergence linked to agricultural intensification and environmental change.. Proc Natl Acad Sci U S A 2013;110(21):8399–8404.
    doi: 10.1073/pnas.1208059110google scholar: lookup
  19. Cortes MC, Cauchemez S, Lefrancq N, Luby SP, Jahangir Hossain M, Sazzad HMS. Characterization of the spatial and temporal distribution of Nipah virus spillover events in Bangladesh, 2007–2013.. J Infect Dis 2018;217(9):1390–1394.
    doi: 10.1093/infdis/jiy015google scholar: lookup
  20. Hawman DW, Feldmann H. Crimean–Congo haemorrhagic fever virus.. Nat Rev Microbiol 2023;21(7):463–477.
  21. Hui DS, Azhar EI, Kim Y‐J, Memish ZA, Oh M‐d, Zumla A. Middle East respiratory syndrome coronavirus: risk factors and determinants of primary, household, and nosocomial transmission.. Lancet Infect Dis 2018;18(8):e217–e227.
  22. Spengler JR, Bergeron É, Rollin PE. Seroepidemiological studies of Crimean‐Congo hemorrhagic fever virus in domestic and wild animals.. PLoS Negl Trop Dis 2016;10(1):e0004210.
  23. Wille M, Barr IG. Resurgence of avian influenza virus.. Science 2022;376(6592):459–460.
    doi: 10.1126/science.abo1232google scholar: lookup
  24. Halpin K, Hyatt AD, Fogarty R, Middleton D, Bingham J, Epstein JH. Pteropid bats are confirmed as the reservoir hosts of henipaviruses: a comprehensive experimental study of virus transmission.. Am J Trop Med Hyg 2011;85(5):946–951.
  25. Edson D, Field HE, McMichael LA, Vidgen M, Goldspink L, Broos A. Routes of Hendra virus excretion in naturally‐infected flying‐foxes: implications for viral transmission and spillover risk.. PLoS One 2015;10(10):15.
  26. Marsh GA, Haining J, Hancock TJ, Robinson R, Foord AJ, Barr JA. Experimental infection of horses with Hendra virus/Australia/horse/2008/Redlands.. Emerg Infect Dis 2011;17(12):2232–2238.
    doi: 10.3201/eid1712.111162google scholar: lookup
  27. Williamson MM, Hooper PT, Selleck PW, Gleeson LJ, Daniels PW, Westbury HA. Transmission studies of Hendra virus (equine morbillivirus) in fruit bats, horses and cats.. Aust Vet J 1998;76(12):813–818.
  28. Hanna JN, McBride WJ, Brookes DL, Shield J, Taylor CT, Smith IL. Hendra virus infection in a veterinarian.. Med J Aust 2006;185(10):562–564.
  29. Murray K, Rogers R, Selvey L, Selleck P, Hyatt A, Gould A. A novel morbillivirus pneumonia of horses and its transmission to humans.. Emerg Infect Dis 1995;1(1):31–33.
    doi: 10.3201/eid0101.950107google scholar: lookup
  30. O'Sullivan JD, Allworth AM, Paterson DL, Snow TM, Boots R, Gleeson LJ. Fatal encephalitis due to novel paramyxovirus transmitted from horses.. Lancet 1997;349(9045):93–95.
  31. Playford EG, McCall B, Smith G, Slinko V, Allen G, Smith I. Human Hendra virus encephalitis associated with equine outbreak, Australia, 2008. Emerg Infect Dis 2010;16(2):219–223.
    doi: 10.3201/eid1602.090552google scholar: lookup
  32. Selvey L, Sheridan J. Outbreak of severe respiratory disease in humans and horses due to a previously unrecognized paramyxovirus. J Travel Med 1995;2(4):275.
  33. Becker DJ, Eby P, Madden W, Peel AJ, Plowright RK. Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts. Ecol Lett 2023;26:23–36.
    doi: 10.1111/ele.14007google scholar: lookup
  34. Eby P, Peel AJ, Hoegh A, Madden W, Giles JR, Hudson PJ. Pathogen spillover driven by rapid changes in bat ecology. Nature 2023;613:340–344.
  35. Faust CL, Castellanos AA, Peel AJ, Eby P, Plowright RK, Han BA. Environmental variation across multiple spatial scales and temporal lags influences Hendra virus spillover. J Appl Ecol 2023;60:1457–1467.
    doi: 10.1111/1365-2664.14415google scholar: lookup
  36. Lunn TJ, Peel AJ, McCallum H, Eby P, Kessler MK, Plowright RK. Spatial dynamics of pathogen transmission in communally roosting species: impacts of changing habitats on bat‐virus dynamics. J Anim Ecol 2021;90:2609–2622.
    doi: 10.1111/1365-2656.13566google scholar: lookup
  37. McFarlane R, Becker N, Field H. Investigation of the climatic and environmental context of Hendra virus spillover events 1994–2010. PLoS One 2011;6(12):e28374.
  38. Smith C, Skelly C, Kung N, Roberts B, Field H. Flying‐fox species density‐a spatial risk factor for Hendra virus infection in horses in eastern Australia. PLoS One 2014;9(6):e99965.
  39. Martin G, Yanez‐Arenas C, Plowright RK, Chen C, Roberts B, Skerratt LF. Hendra virus spillover is a bimodal system driven by climatic factors. Ecohealth 2018;15(3):526–542.
    doi: 10.1007/s10393-017-1309-ygoogle scholar: lookup
  40. Hafi A, Gomboso J, Hean R, Scott F, Arthur T, Rahman N. Estimating the value of Australian biosecurity arrangements for equine influenza since the 2007 outbreak. Canberra: Australian Bureau of Agricultural and Resource Economics and Sciences; 2020.
    doi: 10.25814/sqnqe-k268google scholar: lookup
  41. Linnegar B, Kerlin D, Eby P, Kemsley P, McCallum H, Peel A. Horse populations are severely underestimated in a region at risk of Hendra virus spillover. Aust Vet J 2024;102:342–352.
    doi: 10.1111/avj.13331google scholar: lookup
  42. Business Queensland. Summary of Hendra virus incidents in horses. Queensland: Queensland Government; 2024 [cited 2024 Jun 10].
  43. Australian Bureau of Statistics. Suburbs and localities (Jul2021–Jun2026) 2021. 2021 [cited 2025 Oct 2].
  44. Australian Bureau of Statistics. Search Census data. 2021 [cited 2025 Apr 16].
  45. Geoscape Australia. Administrative boundaries: Commonwealth of Australia. 2023 [cited 2023 Apr13].
  46. Australian Bureau of Statistics. Australian statistical geography standard digital boundary files. 2021 [cited 2024 Feb 15].
  47. State of Queensland Department of Resources. Local government boundaries prior to 140308—Queensland. 2023 [cited 2023 Nov 27].
  48. NSW Department of Customer Service. Spatial services—NSW cadastre. New South Wales: NSW Government; 2024 [cited 2024 Oct 22].
  49. Queensland Department of Resources. Cadastral data weekly—whole of State Queensland. Beenleigh: Queensland Government; 2024 [cited 2024 Oct 21].
  50. Eby P, Peel A, Hoegh A, Madden W, Giles J, Hudson P. Data from: pathogen spillover driven by rapid changes in bat ecology. Dataset C: SEQ monthly roost distribution and population estimates. Ithaca, NY: Cornell University Library eCommons Repository; 2022 [cited 2023 May 31].
  51. Calderón‐Loor M, Hadjikakou M, Bryan BA. High‐resolution wall‐to‐wall land‐cover mapping and land change assessment for Australia from 1985 to 2015. Rem Sens Environ 2021;252:112148.
    doi: 10.1016/j.rse.2020.112148google scholar: lookup
  52. Friedl MA, Sulla‐Menashe D. MODIS/Terra + Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061. Sioux Falls, SD: NASA EOSDIS Land Processes Distributed Active Archive Center; 2022.
    doi: 10.5067/modis/mcd12q1.061google scholar: lookup
  53. Department of Agriculture Fisheries and Forestry. Catchment scale land use of Australia and commodities. Canberra, Australia: Australian Bureau of Agricultural and Resource Economics and Sciences; 2024 [2023 Version 2: cited 2024 Oct 10 ].
  54. Australian Bureau of Statistics. Estimated Resident Population (ERP) 2022–23 financial year 2024. [cited 2024 Oct 19].
  55. Australian Bureau of Statistics. Regional population: Statistics about the population and components of change (births, deaths, migration) for Australia's capital cities and regions. 2021 [cited 2021 Aug 1].
  56. Mu H, Li X, Wen Y, Huang J, Du P, Su W. A global record of annual terrestrial Human Footprint dataset from 2000 to 2018. Sci Data 2022;9(1):176.
  57. Williams BA, Venter O, Allan JR, Atkinson SC, Rehbein JA, Ward M. Change in terrestrial human footprint drives continued loss of intact ecosystems. One Earth 2020;3(3):371–382.
  58. Hijmans RJ, Bivand R, Forner K, Ooms J, Pebesma E, Sumner MD. Package ‘terra’. Vienna, Austria: Maintainer; 2024.
  59. Pebesma E, Bivand R. Spatial data science: with applications in R. New York: Chapman and Hall/CRC; 2023.
    doi: 10.1201/9780429459016google scholar: lookup
  60. R Core Team. R: a language and environment for statistical computing. Vienna, Austria: Foundation for Statistical Computing; 2024.
  61. Wood SN. Generalized additive models: an introduction with R. 2nd ed. New York: Chapman and Hall/CRC; 2017. p. 496.
    doi: 10.1201/9781315370279google scholar: lookup
  62. Zeileis A, Kleiber C, Jackman S. Regression models for count data in R. J Stat Softw 2008;27(8):1–25.
    doi: 10.18637/jss.v027.i08google scholar: lookup
  63. Macdonald SL. Sightings. 1.1 ed. Townsville: Ug Media; 2013.
  64. Wright MN, Ziegler A. ranger: a fast implementation of random forests for high dimensional data in C++ and R. J Stat Softw 2017;77:1–17.
    doi: 10.18637/jss.v077.i01google scholar: lookup
  65. Department of Agriculture Fisheries and Forestry. Hendra virus. 2023 [cited 2025 Feb 17].
  66. Halpin K, Graham K, Durr PA. Sero‐monitoring of horses demonstrates the Equivac HeV Hendra virus vaccine to be highly effective in inducing neutralising antibody titres. Vaccine 2021;9(7):731.
    doi: 10.3390/vaccines9070731google scholar: lookup
  67. Moloney BJ. Overview of the epidemiology of equine influenza in the Australian outbreak. Aust Vet J 2011;89(Suppl 1):50–56.
  68. Nguyen V, Robinson T, Tsiaplias S. The Australian economy in 2023–24: navigating a narrow path. Aust Econ Rev 2024;57(1):5–20.
    doi: 10.1111/1467-8462.12542google scholar: lookup
  69. Tsiaplias S, Wang J. The Australian economy in 2022–23: inflation and higher Interest rates in a post‐COVID‐19 world. Aust Econ Rev 2023;56(1):5–19.
    doi: 10.1111/1467-8462.12498google scholar: lookup
  70. Rural and Regional Affairs and Transport References Committee. Feasibility of a national horse traceability register for all horses. Canberra: Commonwealth of Australia. Parliament of Australia; 2019.
  71. Terletzky P, Ramsey RD. A semi‐automated single day image differencing technique to identify animals in aerial imagery. PLoS One 2014;9:e85239.
  72. Hollings T, Burgman M, van Andel M, Gilbert M, Robinson T, Robinson A. How do you find the green sheep? A critical review of the use of remotely sensed imagery to detect and count animals. Methods Ecol Evol 2018;9(4):881–892.
    doi: 10.1111/2041-210x.12973google scholar: lookup
  73. Cranston A, Cooper N, Bro‐Jørgensen J. Using joint species distribution modelling to identify climatic and non‐climatic drivers of Afrotropical ungulate distributions. Ecography 2024;2024(11):e07209.
    doi: 10.1111/ecog.07209google scholar: lookup
  74. Nicolas G, Robinson TP, Wint GRW, Conchedda G, Cinardi G, Gilbert M. Using random forest to improve the downscaling of global livestock census data. PLoS One 2016;11(3):e0150424.
  75. Gilbert M, Nicolas G, Cinardi G, Van Boeckel TP, Vanwambeke SO, Wint GRW. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci Data 2018;5(1):180227.
    doi: 10.1038/sdata.2018.227google scholar: lookup
  76. Kiffner C, Paciência FMD, Henrich G, Kaitila R, Chuma IS, Mbaryo P. Road‐based line distance surveys overestimate densities of olive baboons. PLoS One 2022;17(2):e0263314.
  77. Marques TA, Buckland ST, Borchers DL, Tosh D, McDonald RA. Point transect sampling along linear features. Biometrics 2010;66(4):1247–1255.
  78. Marques TA, Buckland ST, Bispo R, Howland B. Accounting for animal density gradients using independent information in distance sampling surveys. Stat Methods App 2013;22(1):67–80.
    doi: 10.1007/s10260-012-0223-2google scholar: lookup
  79. Porteus TA, Richardson SM, Reynolds JC. The importance of survey design in distance sampling: field evaluation using domestic sheep. Wildl Res 2011;38(3):221–234.
    doi: 10.1071/wr10234google scholar: lookup
  80. Martin G, Yanez‐Arenas C, Chen C, Plowright RK, Webb RJ, Skerratt LF. Climate change could increase the geographic extent of Hendra virus spillover risk. Ecohealth 2018;15(3):509–525.
    doi: 10.1007/s10393-018-1322-9google scholar: lookup
  81. Williamson KM, Wheeler S, Kerr J, Bennett J, Freeman P, Kohlhagen J. Hendra in the hunter valley. One Health 2020;10:100162.
  82. Taylor J, Thompson K, Annand EJ, Massey PD, Bennett J, Eden J-S. Novel variant Hendra virus genotype 2 infection in a horse in the greater Newcastle region, New South Wales, Australia. One Health 2022;15:100423.
  83. Gibb R, Redding DW, Chin KQ, Donnelly CA, Blackburn TM, Newbold T. Zoonotic host diversity increases in human‐dominated ecosystems. Nature 2020;584(7821):398–402.
    doi: 10.1038/s41586-020-2562-8google scholar: lookup
  84. Zhang L, Rohr J, Cui R, Xin Y, Han L, Yang X. Biological invasions facilitate zoonotic disease emergences. Nat Commun 2022;13(1):1762.
  85. Tomori O, Oluwayelu DO. Domestic animals as potential reservoirs of zoonotic viral diseases. Annu Rev Anim Biosci 2023;11:33–55.
  86. Baker RE, Mahmud AS, Miller F I, Rajeev M, Rasambainarivo F, Rice BL. Infectious disease in an era of global change. Nat Rev Microbiol 2021;20:193–205.

Citations

This article has been cited 0 times.