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The science of the host–virus network

Abstract

Better methods to predict and prevent the emergence of zoonotic viruses could support future efforts to reduce the risk of epidemics. We propose a network science framework for understanding and predicting human and animal susceptibility to viral infections. Related approaches have so far helped to identify basic biological rules that govern cross-species transmission and structure the global virome. We highlight ways to make modelling both accurate and actionable, and discuss the barriers that prevent researchers from translating viral ecology into public health policies that could prevent future pandemics.

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Fig. 1: Designing predictive models.
Fig. 2: Four methods of interrogating host–virus networks.
Fig. 3: Two decades of coronavirus research.

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References

  1. Jones, K. E. et al. Global trends in emerging infectious diseases. Nature 451, 990–993 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Woolhouse, M. E. et al. Temporal trends in the discovery of human viruses. Proc. R. Soc. B 275, 2111–2115 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Smith, K. F. et al. Global rise in human infectious disease outbreaks. J. R. Soc. Interface 11, 20140950 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Carlson, C. J. et al. Climate change will drive novel cross-species viral transmission. Preprint at bioRxiv https://doi.org/10.1101/2020.01.24.918755 (2020).

  5. Swei, A., Couper, L. I., Coffey, L. L., Kapan, D. & Bennett, S. Patterns, drivers, and challenges of vector-borne disease emergence. Vector Borne Zoonotic Dis. 20, 159–170 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Belay, E. D. et al. Zoonotic disease programs for enhancing global health security. Emerg. Infect. Dis. 23, S65 (2017).

    Article  PubMed Central  Google Scholar 

  7. Morse, S. S. et al. Prediction and prevention of the next pandemic zoonosis. Lancet 380, 1956–1965 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Carroll, D. et al. The global virome project. Science 359, 872–874 (2018).

    Article  CAS  PubMed  Google Scholar 

  9. Carlson, C. J., Zipfel, C. M., Garnier, R. & Bansal, S. Global estimates of mammalian viral diversity accounting for host sharing. Nat. Ecol. Evol. 3, 1070–1075 (2019).

    Article  PubMed  Google Scholar 

  10. Babayan, S. A., Orton, R. J. & Streicker, D. G. Predicting reservoir hosts and arthropod vectors from evolutionary signatures in RNA virus genomes. Science 362, 577–580 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Han, B. A. et al. Undiscovered bat hosts of filoviruses. PLoS Negl. Trop. Dis. 10, e0004815 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Schmidt, J. P. et al. Spatiotemporal fluctuations and triggers of Ebola virus spillover. Emerg. Infect. Dis. 23, 415 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Guth, S., Visher, E., Boots, M. & Brook, C. E. Host phylogenetic distance drives trends in virus virulence and transmissibility across the animal–human interface. Phil. Trans. R. Soc. Biol. Sci. 374, 20190296 (2019).

    Article  Google Scholar 

  14. Glennon, E. E. et al. Syndromic detectability of haemorrhagic fever outbreaks. Preprint at medRxiv https://doi.org/10.1101/2020.03.28.20019463 (2020).

  15. Pigott, D. M. et al. Local, national, and regional viral haemorrhagic fever pandemic potential in Africa: a multistage analysis. Lancet 390, 2662–2672 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Palmer, S., Brown, D. & Morgan, D. Early qualitative risk assessment of the emerging zoonotic potential of animal diseases. BMJ 331, 1256–1260 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Grange, Z. L. et al. Ranking the risk of animal-to-human spillover for newly discovered viruses. Proc. Natl Acad. Sci. USA 118, e2002324118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Carlson, C. J. From PREDICT to prevention, one pandemic later. Lancet Microbe 1, e6–e7 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Holmes, E., Rambaut, A. & Andersen, K. Pandemics: spend on surveillance, not prediction. Nature 558, 180–182 (2018).

    Article  CAS  PubMed  Google Scholar 

  20. Breiman, L. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16, 199–231 (2001).

    Article  Google Scholar 

  21. Mouquet, N. et al. Predictive ecology in a changing world. J. Appl. Ecol. 52, 1293–1310 (2015).

    Article  Google Scholar 

  22. Olival, K. J. et al. Host and viral traits predict zoonotic spillover from mammals. Nature 546, 646–650 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Stephens, P. R. et al. Global mammal parasite database version 2.0. Ecology 98, 1476 (2017).

    Article  PubMed  Google Scholar 

  24. Wardeh, M., Risley, C., McIntyre, M. K., Setzkorn, C. & Baylis, M. Database of host–pathogen and related species interactions, and their global distribution. Sci. Data 2, 150049 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Shaw, L. P. et al. The phylogenetic range of bacterial and viral pathogens of vertebrates. Mol. Ecol. 29, 3361–3379 (2020).

    Article  PubMed  Google Scholar 

  26. Gibb, R. et al. Data proliferation, reconciliation, and synthesis in viral ecology. BioScience https://doi.org/10.1093/biosci/biab080 (2021).

  27. Dallas, T., Park, A. W. & Drake, J. M. Predicting cryptic links in host–parasite networks. PLoS Comput. Biol. 13, e1005557 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Poisot, T. et al. Imputing the mammalian virome with linear filtering and singular value decomposition. Preprint at https://arxiv.org/abs/2105.14973 (2021).

  29. Carlson, C. J. et al. The Global Virome in One Network (VIRION): an atlas of vertebrate–virus associations. Preprint at bioRxiv https://doi.org/10.1101/2021.08.06.455442 (2021).

  30. Albery, G. F., Eskew, E. A., Ross, N. & Olival, K. J. Predicting the global mammalian viral sharing network using phylogeography. Nat. Commun. 11, 2260 (2020).

  31. Davies, T. J. & Pedersen, A. B. Phylogeny and geography predict pathogen community similarity in wild primates and humans. Proc. R. Soc. B Biol. Sci. 275, 1695–1701 (2008).

    Article  Google Scholar 

  32. Guy, C., Thiagavel, J., Mideo, N. & Ratcliffe, J. M. Phylogeny matters: revisiting ‘a comparison of bats and rodents as reservoirs of zoonotic viruses’. R. Soc. Open Sci. 6, 181182 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Washburne, A. D. et al. Taxonomic patterns in the zoonotic potential of mammalian viruses. PeerJ 6, e5979 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Plowright, R. K. et al. Pathways to zoonotic spillover. Nat. Rev. Microbiol. 15, 502 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Stephens, P. R. et al. The macroecology of infectious diseases: a new perspective on global-scale drivers of pathogen distributions and impacts. Ecol. Lett. 19, 1159–1171 (2016).

    Article  PubMed  Google Scholar 

  36. Longdon, B., Brockhurst, M. A., Russell, C. A., Welch, J. J. & Jiggins, F. M. The evolution and genetics of virus host shifts. PLoS Pathog. 10, e1004395 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Farrell, M. J., Elmasri, M., Stephens, D. A. & Davies, T. J. Predicting missing links in global host–parasite networks. bioRxiv https://doi.org/10.1101/2020.02.25.965046 (2020).

  38. Gilbert, A. T. et al. Deciphering serology to understand the ecology of infectious diseases in wildlife. EcoHealth 10, 298–313 (2013).

    Article  PubMed  Google Scholar 

  39. Becker, D. J., Seifert, S. N. & Carlson, C. J. Beyond infection: integrating competence into reservoir host prediction. Trends Ecol. Evol. 35, 1062–1065 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Walsh, M. G., Mor, S. M., Maity, H. & Hossain, S. A preliminary ecological profile of Kyasanur Forest disease virus hosts among the mammalian wildlife of the Western Ghats, India. Ticks Tick Borne Dis. 11, 101419 (2020).

    Article  PubMed  Google Scholar 

  41. Plowright, R. K. et al. Prioritizing surveillance of Nipah virus in India. PLoS Negl. Trop. Dis. 13, e0007393 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Schmidt, J. P. et al. Ecological indicators of mammal exposure to Ebolavirus. Philos. Trans. R. Soc. B Biol. Sci. 374, 20180337 (2019).

    Article  Google Scholar 

  43. Worsley-Tonks, K. E. et al. Using host traits to predict reservoir host species of rabies virus. PLoS Negl. Trop. Dis. 14, e0008940 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Woolhouse, M. E. & Gowtage-Sequeria, S. Host range and emerging and reemerging pathogens. Emerg. Infect. Dis. 11, 1842 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Johnson, C. K. et al. Spillover and pandemic properties of zoonotic viruses with high host plasticity. Sci. Rep. 5, 14830 (2015).

    Article  Google Scholar 

  46. Elena, S. F. & Sanjuán, R. Adaptive value of high mutation rates of RNA viruses: separating causes from consequences. J. Virol. 79, 11555–11558 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Duffy, S. Why are RNA virus mutation rates so damn high? PLoS Biol. 16, e3000003 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Grewelle, R. E. Larger viral genome size facilitates emergence of zoonotic diseases. Preprint at bioRxiv https://doi.org/10.1101/2020.03.10.986109 (2020).

  49. Mollentze, N. & Streicker, D. G. Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts. Proc. Natl Acad. Sci. USA 117, 9423–9430 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Walker, J. W., Han, B. A., Ott, I. M. & Drake, J. M. Transmissibility of emerging viral zoonoses. PLoS ONE 13, e0206926 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Damas, J. et al. Broad host range of SARS-CoV-2 predicted by comparative and structural analysis of ACE2 in vertebrates. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2010146117 (2020).

  52. Zhang, Z. et al. Rapid identification of human-infecting viruses. Transbound. Emerg. Dis. 66, 2517–2522 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Eng, C. L., Tong, J. C. & Tan, T. W. Predicting zoonotic risk of influenza A viruses from host tropism protein signature using random forest. Int. J. Mol. Sci. 18, 1135 (2017).

    Article  PubMed Central  Google Scholar 

  54. Li, J. et al. Machine learning methods for predicting human-adaptive influenza A viruses based on viral nucleotide compositions. Mol. Biol. Evol. 37, 1224–1236 (2020).

    Article  CAS  PubMed  Google Scholar 

  55. Kim, B., Niu, X., Hunter, D. R. & Cao, X. A dynamic additive and multiplicative effects model with application to the United Nations voting behaviors. Preprint at https://arxiv.org/abs/1803.06711 (2018).

  56. Becker, D. et al. Optimizing predictive models to prioritize viral discovery in zoonotic reservoirs. Lancet Microbe (in the press).

  57. Han, B. A., Schmidt, J. P., Bowden, S. E. & Drake, J. M. Rodent reservoirs of future zoonotic diseases. Proc. Natl Acad. Sci. USA 112, 7039–7044 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Plourde, B. T. et al. Are disease reservoirs special? Taxonomic and life history characteristics. PLoS ONE 12, e0180716 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Keesing, F. et al. Impacts of biodiversity on the emergence and transmission of infectious diseases. Nature 468, 647–652 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Albery, G. F. & Becker, D. J. Fast-lived hosts and zoonotic risk. Trends Parasitol. 37, 117–129 (2021).

    Article  CAS  PubMed  Google Scholar 

  61. Young, C. C. & Olival, K. J. Optimizing viral discovery in bats. PLoS ONE 11, e0149237 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Albery, G. F. et al. Urban-adapted mammal species have more known pathogens. Preprint at bioRxiv https://doi.org/10.1101/2021.01.02.425084 (2021).

  63. Wille, M., Geoghegan, J. L. & Holmes, E. C. How accurately can we assess zoonotic risk? PLoS Biol. 19, e3001135 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Gibb, R. et al. Mammal virus diversity estimates are unstable due to accelerating discovery effort. Preprint at bioRxiv https://doi.org/10.1101/2021.08.10.455791 (2021).

  65. Xu, G. J. et al. Comprehensive serological profiling of human populations using a synthetic human virome. Science 348, aaa0698 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Geoghegan, J. L. & Holmes, E. C. Predicting virus emergence amid evolutionary noise. Open Biol. 7, 170189 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Fischhoff, I. R., Castellanos, A. A., Rodrigues, J. P., Varsani, A. & Han, B. A. Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2021.1651 (2021).

  68. Hou, Y. et al. Angiotensin-converting enzyme 2 (ACE2) proteins of different bat species confer variable susceptibility to SARS-CoV entry. Arch. Virol. 155, 1563–1569 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Thompson, A. J., de Vries, R. P. & Paulson, J. C. Virus recognition of glycan receptors. Curr. Opin. Virol. 34, 117–129 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Kocher, J. F. et al. Bat caliciviruses and human noroviruses are antigenically similar and have overlapping histo-blood group antigen binding profiles. Mbio 9, e00869-18 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Chiramel, A. I. et al. TRIM5α restricts flavivirus replication by targeting the viral protease for proteasomal degradation. Cell Rep. 27, 3269–3283 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Young, F., Rogers, S. & Robertson, D. L. Predicting host taxonomic information from viral genomes: a comparison of feature representations. PLoS Comput. Biol. 16, e1007894 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Truong, P., Garcia-Vallve, S. & Puigbo, P. An unsupervised algorithm for host identification in flaviviruses. Life https://doi.org/10.3390/life11050442 (2021).

  76. Mollentze, N., Babayan, S. & Streicker, D. Identifying and prioritizing potential human-infecting viruses from their genome sequences. PLoS Biol. 19, e3001390 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Wang, W. et al. A network-based integrated framework for predicting virus–prokaryote interactions. NAR Genom. Bioinform. 2, lqaa044 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Bartoszewicz, J. M., Seidel, A. & Renard, B. Y. Interpretable detection of novel human viruses from genome sequencing data. NAR Genom. Bioinform. 3, lqab004 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  79. He, X. et al. Neural collaborative filtering. In Proc. 26th International Conference on World Wide Web 26, 173–182 (Republic and Canton of Geneva, Switzerland, 2017).

  80. Fout, A., Byrd, J., Shariat, B. & Ben-Hur, A. Protein interface prediction using graph convolutional networks. NIPS’17: Proc. 31st International Conference on Neural Information Processing Systems 31, 6533–6542 (2017).

    Google Scholar 

  81. Hamilton, W. L., Ying, R. & Leskovec, J. Representation learning on graphs: methods and applications. IEEE Data Eng. Bull. 40, 52–74 (2017).

    Google Scholar 

  82. Bergner, L. M. et al. Characterizing and evaluating the zoonotic potential of novel viruses discovered in vampire bats. Viruses 13, 252 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Dietze, M. C. et al. Iterative near-term ecological forecasting: needs, opportunities, and challenges. Proc. Natl Acad. Sci. USA 115, 1424–1432 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Schulz, J. E. et al. Serological evidence for henipa-like and filo-like viruses in Trinidad bats. J. Infect. Dis. 221, S375–S382 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Brook, C. E. et al. Disentangling serology to elucidate henipa- and filovirus transmission in Madagascar fruit bats. J. Anim. Ecol. 88, 1001–1016 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Seifert, S. N. et al. Rousettus aegyptiacus bats do not support productive Nipah virus replication. J. Infect. Dis. 221, S407–S413 (2020).

    Article  CAS  PubMed  Google Scholar 

  87. Carlson, C. J. et al. The future of zoonotic risk prediction. Phil. Trans. R. Soc. B Biol. Sci. 376, 20200358 (2021).

    Article  Google Scholar 

  88. Ge, X.-Y. et al. Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor. Nature 503, 535–538 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Menachery, V. D. et al. A SARS-like cluster of circulating bat coronaviruses shows potential for human emergence. Nat. Med. 21, 1508–1513 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Guan, Y. et al. Isolation and characterization of viruses related to the SARS coronavirus from animals in southern China. Science 302, 276–278 (2003).

    Article  CAS  PubMed  Google Scholar 

  91. Woo, P. C. Y. et al. Characterization and complete genome sequence of a novel coronavirus, coronavirus HKU1, from patients with pneumonia. J. Virol. 79, 884–895 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Li, W. et al. Bats are natural reservoirs of SARS-like coronaviruses. Science 310, 676–679 (2005).

    Article  CAS  PubMed  Google Scholar 

  93. Wang, M. et al. SARS-CoV infection in a restaurant from palm civet. Emerg. Infect. Dis. 11, 1860–1865 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Hu, B. et al. Discovery of a rich gene pool of bat SARS-related coronaviruses provides new insights into the origin of SARS coronavirus. PLoS Pathog. 13, e1006698 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Zhou, P. et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270–273 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Xiao, K. et al. Isolation of SARS-CoV-2-related coronavirus from Malayan pangolins. Nature 583, 286–289 (2020).

    Article  CAS  PubMed  Google Scholar 

  97. Lam, T.-Y. et al. Identifying SARS-CoV-2-related coronaviruses in Malayan pangolins. Nature 583, 282–285 (2020).

    Article  CAS  PubMed  Google Scholar 

  98. Wacharapluesadee, S. et al. Evidence for SARS-CoV-2 related coronaviruses circulating in bats and pangolins in Southeast Asia. Nat. Commun. 12, 972 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Holmes, E. C. et al. The origins of SARS-CoV-2: a critical review. Cell 184, 4848–4856 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Oude Munnink, B. B. et al. Transmission of SARS-CoV-2 on mink farms between humans and mink and back to humans. Science 371, 172–177 (2021).

    Article  CAS  PubMed  Google Scholar 

  101. Chandler, J. C. et al. SARS-CoV-2 exposure in wild white-tailed deer (Odocoileus virginianus). Proc. Natl Acad. Sci. USA 118, e2114828118 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Jia, P., Dai, S., Wu, T. & Yang, S. New approaches to anticipate the risk of reverse zoonosis. Trends Ecol. Evol. 36, 580–590 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  103. Lednicky, J. A. et al. Isolation of a novel recombinant canine coronavirus from a visitor to Haiti: further evidence of transmission of coronaviruses of zoonotic origin to humans. Clin. Infect. Dis. https://doi.org/10.1093/cid/ciab924 (2021).

  104. Vlasova, A. N. et al. Novel canine coronavirus isolated from a hospitalized pneumonia patient, East Malaysia. Clin. Infect. Dis. https://doi.org/10.1093/cid/ciab456 (2021).

  105. Lednicky, J. A. et al. Emergence of porcine delta-coronavirus pathogenic infections among children in Haiti through independent zoonoses and convergent evolution. Preprint at medRxiv https://doi.org/10.1101/2021.03.19.21253391 (2021).

  106. Hay, A. J. & McCauley, J. W. The WHO global influenza surveillance and response system (GISRS)—a future perspective. Influenza Other Respir. Viruses 12, 551–557 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  107. Subbarao, K. et al. Characterization of an avian influenza A (H5N1) virus isolated from a child with a fatal respiratory illness. Science 279, 393–396 (1998).

    Article  CAS  PubMed  Google Scholar 

  108. Kandeel, A. et al. Zoonotic transmission of avian influenza virus (H5N1), Egypt, 2006–2009. Emerg. Infect. Dis. 16, 1101 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  109. Ke, C. et al. Human infection with highly pathogenic avian influenza A (H7N9) virus, China. Emerg. Infect. Dis. 23, 1332 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Gaidet, N. et al. Evidence of infection by H5N2 highly pathogenic avian influenza viruses in healthy wild waterfowl. PLoS Pathog. 4, e1000127 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  111. Webster, R. G., Bean, W. J., Gorman, O. T., Chambers, T. M. & Kawaoka, Y. Evolution and ecology of influenza A viruses. Microbiol. Mol. Biol. Rev. 56, 152–179 (1992).

    CAS  Google Scholar 

  112. Pawar, S. D. et al. Avian influenza surveillance reveals presence of low pathogenic avian influenza viruses in poultry during 2009–2011 in the West Bengal State, India. Virol. J. 9, 151 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  113. Parry, R., Wille, M., Turnbull, O. M., Geoghegan, J. L. & Holmes, E. C. Divergent influenza-like viruses of amphibians and fish support an ancient evolutionary association. Viruses 12, 1042 (2020).

    Article  CAS  PubMed Central  Google Scholar 

  114. Campbell, P. J. et al. The M segment of the 2009 pandemic influenza virus confers increased neuraminidase activity, filamentous morphology, and efficient contact transmissibility to A/Puerto Rico/8/1934-based reassortant viruses. J. Virol. 88, 3802–3814 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  115. Carlson, C. Evolutionary surprise, artificial intelligence, and H5N8. The Verena Blog https://www.viralemergence.org/blog/evolutionary-surprise-artificial-intelligence-and-h5n8 (2021).

  116. Wardeh, M., Baylis, M. & Blagrove, M. S. Predicting mammalian hosts in which novel coronaviruses can be generated. Nat. Commun. 12, 780 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Crossman, L. C. Leveraging deep learning to simulate coronavirus spike proteins has the potential to predict future zoonotic sequences. Preprint at bioRxiv https://doi.org/10.1101/2020.04.20.046920 (2020).

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Acknowledgements

The Viral Emergence Research Initiative (VERENA) consortium is supported by NSF BII 2021909. For more information, see viralemergence.org.

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C.J.C. and G.F.A. conceived the study and drafted the manuscript, G.F.A. and C.J.C. produced visualizations, and all authors contributed to the writing.

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Correspondence to Gregory F. Albery or Colin J. Carlson.

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Albery, G.F., Becker, D.J., Brierley, L. et al. The science of the host–virus network. Nat Microbiol 6, 1483–1492 (2021). https://doi.org/10.1038/s41564-021-00999-5

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