Host and viral traits predict zoonotic spillover from mammals

  • An Erratum to this article was published on 23 August 2017

Abstract

The majority of human emerging infectious diseases are zoonotic, with viruses that originate in wild mammals of particular concern (for example, HIV, Ebola and SARS)1,2,3. Understanding patterns of viral diversity in wildlife and determinants of successful cross-species transmission, or spillover, are therefore key goals for pandemic surveillance programs4. However, few analytical tools exist to identify which host species are likely to harbour the next human virus, or which viruses can cross species boundaries5,6,7. Here we conduct a comprehensive analysis of mammalian host–virus relationships and show that both the total number of viruses that infect a given species and the proportion likely to be zoonotic are predictable. After controlling for research effort, the proportion of zoonotic viruses per species is predicted by phylogenetic relatedness to humans, host taxonomy and human population within a species range—which may reflect human–wildlife contact. We demonstrate that bats harbour a significantly higher proportion of zoonotic viruses than all other mammalian orders. We also identify the taxa and geographic regions with the largest estimated number of ‘missing viruses’ and ‘missing zoonoses’ and therefore of highest value for future surveillance. We then show that phylogenetic host breadth and other viral traits are significant predictors of zoonotic potential, providing a novel framework to assess if a newly discovered mammalian virus could infect people.

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Figure 1: Observed viral richness in mammals.
Figure 2: Host traits that predict total viral richness (top row) and proportion of zoonotic viruses (bottom row) per wild mammal species.
Figure 3: Global distribution of the predicted number of ‘missing zoonoses’ by order.
Figure 4: Traits that predict zoonotic potential of a virus.

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Acknowledgements

This research was supported by the United States Agency for International Development (USAID) Emerging Pandemic Threats PREDICT program; and NIH NIAID awards R01AI079231 and R01AI110964. The authors thank C. N. Basaraba, J. Baxter, L. Brierley, E. A. Hagan, J. Levinson, E. H. Loh, L. Mendiola, N. Wale and A. R. Willoughby for assistance with data collection, and B. M. Bolker, A. R. Ives, K. E. Jones, C. K. Johnson, A. M. Kilpatrick, J. A. K. Mazet and M. E. J. Woolhouse for comments.

Author information

K.J.O., T.L.B. and P.D. designed the study and supervised the collection of data. N.R., P.R.H. and K.J.O. designed the statistical approach, wrote the code, and generated figures. K.J.O. performed phylogenetic analyses. C.Z.-T. performed spatial analyses. All authors were involved in writing the manuscript.

Correspondence to Kevin J. Olival or Peter Daszak.

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Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks J. Dushoff and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Figure 1 Conceptual model of zoonotic spillover, viral richness, and summary of models.

a, Conceptual model of zoonotic spillover showing primary risk factors examined, colour-coded according to generalized additive models used. b, Conceptual model of observed, predicted, and actual viral richness in mammals. c, GAMs used in our study to address specific components of a and b, colour-coded by model. Variables listed with ‘or’ under each GAM covaried and were provided as competing terms in model selection, and those in bold were included in the best-fit model using all host–virus associations. Significant variables from each best-fit GAM are noted with an asterisk. Zoonotic viral spillover first depends on the underlying total viral richness in mammal populations and the ecological, taxonomic, and life-history traits that govern this diversity (GAM 1). Second, host- and virus-specific factors may facilitate viral spillover. We examine the relative importance of host phylogenetic distance to humans, ecological opportunity for contact, or other species-specific life-history and taxonomic traits (GAM 2), and identify viral traits associated with a higher likelihood of an observed virus being zoonotic (GAM 3). We estimate the total and zoonotic viral richness per host species using GAMs 1 and 2, and calculate the missing viruses and missing zoonoses under a scenario of increased research effort (b, Methods). Owing to imperfect surveillance in both humans and wildlife and biases in viral detection, there may be uncertainty in the exact proportion of viruses that are zoonotic (b, light grey), and also between the actual, or true, viral richness (dotted lines) and the predicted maximum viral richness per host (dashed line).

Extended Data Figure 2 Heat map of observed total viral richness by mammalian order and viral family.

Dataset includes 754 mammalian species and 586 unique ICTV recognized viral species. Heat map aggregated by rows and columns to group taxa with similar levels of observed viral richness.

Extended Data Figure 3 Global distribution of viral and host species richness for all wild mammals.

a, Observed total viral richness (for n = 576 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 584); e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3a); g, global mammal species richness (n = 5,290); h, mammal richness for species in our database (n = 753); i, mammal species with no described viruses in the literature. Warmer colours (larger values) in panels c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in mammals. Red/pink colours in panel i highlight areas with poor viral surveillance in mammal species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).

Extended Data Figure 4 Global distribution of viral and host species richness for wild carnivores (order Carnivora).

a, Observed total viral richness (for n = 55 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 55); e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3b); g, global host species richness for Carnivora (n = 276); h, host species richness for Carnivora in our database (n = 79); i, species of the order Carnivora with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in carnivores. Red/pink colours in panel i highlight areas with poor viral surveillance in carnivore species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).

Extended Data Figure 5 Global distribution of viral and host species richness for wild even-toed ungulates (order Cetartiodactyla).

a, Observed total viral richness (for n = 70 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 70); e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3c); g, global host species richness for Cetartiodactyla (n = 229); h, host species richness for Cetartiodactyla in our database (n = 105); i, species of the order Cetartiodactyla with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in even-toed ungulates. Red/pink colours in panel i highlight areas with poor viral surveillance in even-toed ungulates species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).

Extended Data Figure 6 Global distribution of viral and host species richness for bats (order Chiroptera).

a, Observed total viral richness (for n = 156 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 157); e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3d); g, global host species richness for Chiroptera (n = 1117); h, host species richness for Chiroptera in our database (n = 192); i, species of the order Chiroptera with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in bats. Red/pink colours in panel i highlight areas with poor viral surveillance in bat species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).

Extended Data Figure 7 Global distribution of viral and host species richness for primates (order Primates).

a, Observed total viral richness (for n = 71 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 73); e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3e); g, global host species richness for Primates (n = 400); h, host species richness for Primates in our database (n = 98); i, primate species with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in primates. Red/pink colours in panel i highlight areas with poor viral surveillance in primate species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).

Extended Data Figure 8 Global distribution of viral and host species richness for rodents (order Rodentia).

a, Observed total viral richness (for n = 178 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 183); e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3f); g, global host species richness for Rodentia (n = 2206); h, host species richness for Rodentia in our database (n = 221); i, rodent species with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in wild rodents. Red/pink colours in panel i highlight areas with poor viral surveillance in rodent species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).

Extended Data Figure 9 Order-level phylogenies showing residuals from zoonoses model.

ae, Subtrees from cytochrome b maximum likelihood phylogeny for 558 mammal species (constrained to order-level topology of mammal supertree) for bats (a), carnivores (b), even-toed ungulates (c), rodents (d) and primates (e). Species included have at least one described virus association and available genetic data. Wildlife species names and terminal branches are colour-coded by the residuals (predicted minus observed) from the best-fit GAM to predict the number of zoonotic viruses using all data. Species with residual values between −1 and 1 (black) are accurately predicted within one virus. Warm colours represent species with positive residuals (orange >1 to 3; red >3). Cool colours represent species with negative residuals (green <−1 to −3; blue <−3). Marine mammals, domestic animals, and species with missing data and not included in the best-fit models are shown in grey.

Extended Data Table 1 Summary of best-fit GAMs for total and zoonotic viral richness per wild mammal species, and probability of a virus being zoonotic

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Olival, K., Hosseini, P., Zambrana-Torrelio, C. et al. Host and viral traits predict zoonotic spillover from mammals. Nature 546, 646–650 (2017). https://doi.org/10.1038/nature22975

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