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Analysis of a comprehensive database of mammalian host–virus relationships reveals that both the total number of viruses that infect a given species and the proportion likely to be zoonotic are predictable and that this enables identification of mammalian species and geographic locations where novel zoonoses are likely to be found. Zoonotic virus distribution patterns Zoonotic viruses, many originating in wild mammals, pose a serious threat to global public health. Peter Daszak and colleagues create a comprehensive database of mammalian host–virus relationships, which they analyse to determine patterns of virus and zoonotic virus distribution in mammals. They identify various factors that influence the number and diversity of viruses that infect a given species as well as factors that predict the proportion of zoonotic viruses per species. In doing so, they identify mammalian species and geographic locations where novel zoonoses are likely to be found. 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 programs 4 . However, few analytical tools exist to identify which host species are likely to harbour the next human virus, or which viruses can cross species boundaries 5 , 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.

Animal and plant species differ dramatically in their quality as hosts for multi-host pathogens, but the causes of this variation are poorly understood. A group of small mammals, including small rodents and shrews, are among the most competent natural reservoirs for three tick-borne zoonotic pathogens, Borrelia burgdorferi, Babesia microti, and Anaplasma phagocytophilum, in eastern North America. For a group of nine commonly-infected mammals spanning >2 orders of magnitude in body mass, we asked whether life history features or surrogates for (unknown) encounter rates with ticks, predicted reservoir competence for each pathogen. Life history features associated with a fast pace of life generally were positively correlated with reservoir competence. However, a model comparison approach revealed that host population density, as a proxy for encounter rates between hosts and pathogens, generally received more support than did life history features. The specific life history features and the importance of host population density differed somewhat between the different pathogens. We interpret these results as supporting two alternative but non-exclusive hypotheses for why ecologically widespread, synanthropic species are often the most competent reservoirs for multi-host pathogens. First, multi-host pathogens might adapt to those hosts they are most likely to experience, which are likely to be the most abundant and/or frequently bitten by tick vectors. Second, species with fast life histories might allocate less to certain immune defenses, which could increase their reservoir competence. Results suggest that of the host species that might potentially be exposed, those with comparatively high population densities, small bodies, and fast pace of life will often be keystone reservoirs that should be targeted for surveillance or management.

The distributions of most infectious agents causing disease in humans are poorly resolved or unknown. However, poorly known and unknown agents contribute to the global burden of disease and will underlie many future disease risks. Existing patterns of infectious disease co-occurrence could thus play a critical role in resolving or anticipating current and future disease threats. We analyzed the global occurrence patterns of 187 human infectious diseases across 225 countries and seven epidemiological classes (human-specific, zoonotic, vector-borne, non–vector-borne, bacterial, viral, and parasitic) to show that human infectious diseases exhibit distinct spatial grouping patterns at a global scale. We demonstrate, using outbreaks of Ebola virus as a test case, that this spatial structuring provides an untapped source of prior information that could be used to tighten the focus of a range of health-related research and management activities at early stages or in data-poor settings, including disease surveillance, outbreak responses, or optimizing pathogen discovery. In examining the correlates of these spatial patterns, among a range of geographic, epidemiological, environmental, and social factors, mammalian biodiversity was the strongest predictor of infectious disease co-occurrence overall and for six of the seven disease classes examined, giving rise to a striking congruence between global pathogeographic and “Wallacean” zoogeographic patterns. This clear biogeographic signal suggests that infectious disease assemblages remain fundamentally constrained in their distributions by ecological barriers to dispersal or establishment, despite the homogenizing forces of globalization. Pathogeography thus provides an overarching context in which other factors promoting infectious disease emergence and spread are set.

Disease may play a critical role in invasions by nonnative plants and animals that currently threaten global biodiversity. For example, a generalist viral pathogen has been recently implicated in one of the most extensive plant invasions worldwide, the invasion and domination of California's perennial grasslands by exotic annual grasses. To date, disease has never been quantitatively assessed as a cause of this invasion. Using a model with field-estimated parameters, we demonstrate that pathogen presence was necessary to reverse competitive outcome and to allow exotic annual grass invasion and dominance. Although pathogen-induced reversal of a competitive hierarchy has been suggested as a mechanism of species invasion, here we quantitatively demonstrate the importance of this phenomenon by using field-derived parameters in a dynamical model. Pathogen-mediated reversals in competitive balance may be critically important for understanding past, and predicting future, invasions.

High-profile epidemics such as Ebola, avian influenza, and severe acute respiratory syndrome (SARS) repeatedly thrust infectious diseases into the limelight. Because the emergence of dis-eases involves so many factors, the need for interdisciplinary approaches to studying emerging infections, particularly those originating from animals (i.e., zoonoses), is frequently discussed. However, effective integration across disciplines is challenging in practice. Ecological ideas, for example, are rarely considered in biomedical research, while insights from biomedicine are often neglected in ecological studies of infectious diseases. One practical reason for this is that researchers in these fields focus on vastly different scales of biological organization, which are difficult to bridge both intellectually and methodologically. Nevertheless, integration across biological scales is increasingly needed for solving the complex problems zoonotic diseases pose to human and animal well-being. Motivated by current events, we use Ebola virusas a case study to highlight fundamental questions about zoonoses that can be addressed by integrating insights and approaches across scales.

The majority of emerging zoonoses originate in wildlife, and many are caused by viruses. However, there are no rigorous estimates of total viral diversity (here termed “virodiversity”) for any wildlife species, despite the utility of this to future surveillance and control of emerging zoonoses. In this case study, we repeatedly sampled a mammalian wildlife host known to harbor emerging zoonotic pathogens (the Indian Flying Fox, Pteropus giganteus ) and used PCR with degenerate viral family-level primers to discover and analyze the occurrence patterns of 55 viruses from nine viral families. We then adapted statistical techniques used to estimate biodiversity in vertebrates and plants and estimated the total viral richness of these nine families in P. giganteus to be 58 viruses. Our analyses demonstrate proof-of-concept of a strategy for estimating viral richness and provide the first statistically supported estimate of the number of undiscovered viruses in a mammalian host. We used a simple extrapolation to estimate that there are a minimum of 320,000 mammalian viruses awaiting discovery within these nine families, assuming all species harbor a similar number of viruses, with minimal turnover between host species. We estimate the cost of discovering these viruses to be ~ $6.3 billion (or ~$ 1.4 billion for 85% of the total diversity), which if annualized over a 10-year study time frame would represent a small fraction of the cost of many pandemic zoonoses. IMPORTANCE Recent years have seen a dramatic increase in viral discovery efforts. However, most lack rigorous systematic design, which limits our ability to understand viral diversity and its ecological drivers and reduces their value to public health intervention. Here, we present a new framework for the discovery of novel viruses in wildlife and use it to make the first-ever estimate of the number of viruses that exist in a mammalian host. As pathogens continue to emerge from wildlife, this estimate allows us to put preliminary bounds around the potential size of the total zoonotic pool and facilitates a better understanding of where best to allocate resources for the subsequent discovery of global viral diversity. Recent years have seen a dramatic increase in viral discovery efforts. However, most lack rigorous systematic design, which limits our ability to understand viral diversity and its ecological drivers and reduces their value to public health intervention. Here, we present a new framework for the discovery of novel viruses in wildlife and use it to make the first-ever estimate of the number of viruses that exist in a mammalian host. As pathogens continue to emerge from wildlife, this estimate allows us to put preliminary bounds around the potential size of the total zoonotic pool and facilitates a better understanding of where best to allocate resources for the subsequent discovery of global viral diversity.

Most species host multiple pathogens, yet field studies rarely examine the processes determining pathogen diversity within a single host or the effects of coinfection on pathogen dynamics in natural systems. Coinfection can affect pathogen transmission and virulence. In turn, coinfection can be regulated within hosts by interactions such as cross‐protective immunity or at broader spatial scales via vector distributions. Using a general model, we demonstrate that coinfection by a group of vectored pathogens is highest with abundant generalist vectors and weak cross‐protection and coinfection‐induced mortality. Using these predictions, we investigate the distribution of five coexisting aphid‐vectored, viral pathogens (barley and cereal yellow dwarf luteoviruses and poleroviruses) in a native perennial grass (Elymus glaucus) in both space (700 km) and time (4 years). Observed coinfection rates were much higher than expected at random, suggesting that within‐host processes exerted weak effects on within‐host pathogen diversity. Covariance among viruses in space and time was highest for viral species sharing a vector. Temporal correlation arose from the synchronous invasion of two viruses transmitted by a shared aphid species. On the basis of our modeling and empirical results, we expect that factors external to individual hosts may affect the coinfection dynamics in other communities hosting vectored pathogens.

Influenza virus surveillance, poultry outbreak investigations and genomic sequencing were assessed to understand the ecology and evolution of low pathogenicity avian influenza (LPAI) A viruses in Bangladesh from 2007 to 2013. We analyzed 506 avian specimens collected from poultry in live bird markets and backyard flocks to identify influenza A viruses. Virus isolation-positive specimens (n = 50) were subtyped and their coding-complete genomes were sequenced. The most frequently identified subtypes among LPAI isolates were H9N2, H11N3, H4N6, and H1N1. Less frequently detected subtypes included H1N3, H2N4, H3N2, H3N6, H3N8, H4N2, H5N2, H6N1, H6N7, and H7N9. Gene sequences were compared to publicly available sequences using phylogenetic inference approaches. Among the 14 subtypes identified, the majority of viral gene segments were most closely related to poultry or wild bird viruses commonly found in Southeast Asia, Europe, and/or northern Africa. LPAI subtypes were distributed over several geographic locations in Bangladesh, and surface and internal protein gene segments clustered phylogenetically with a diverse number of viral subtypes suggesting extensive reassortment among these LPAI viruses. H9N2 subtype viruses differed from other LPAI subtypes because genes from these viruses consistently clustered together, indicating this subtype is enzootic in Bangladesh. The H9N2 strains identified in Bangladesh were phylogenetically and antigenically related to previous human-derived H9N2 viruses detected in Bangladesh representing a potential source for human infection. In contrast, the circulating LPAI H5N2 and H7N9 viruses were both phylogenetically and antigenically unrelated to H5 viruses identified previously in humans in Bangladesh and H7N9 strains isolated from humans in China. In Bangladesh, domestic poultry sold in live bird markets carried a wide range of LPAI virus subtypes and a high diversity of genotypes. These findings, combined with the seven year timeframe of sampling, indicate a continuous circulation of these viruses in the country.

Controlling the pandemic spread of newly emerging diseases requires rapid, targeted allocation of limited resources among nations. Critical, early control steps would be greatly enhanced if the key risk factors can be identified that accurately predict early disease spread immediately after emergence. Here, we examine the role of travel, trade, and national healthcare resources in predicting the emergence and initial spread of 2009 A/H1N1 influenza. We find that incorporating national healthcare resource data into our analyses allowed a much greater capacity to predict the international spread of this virus. In countries with lower healthcare resources, the reporting of 2009 A/H1N1 cases was significantly delayed, likely reflecting a lower capacity for testing and reporting, as well as other socio-political issues. We also report substantial international trade in live swine and poultry in the decade preceding the pandemic which may have contributed to the emergence and mixed genotype of this pandemic strain. However, the lack of knowledge of recent evolution of each H1N1 viral gene segment precludes the use of this approach to determine viral origins. We conclude that strategies to prevent pandemic influenza virus emergence and spread in the future should include: 1) enhanced surveillance for strains resulting from reassortment in traded livestock; 2) rapid deployment of control measures in the initial spreading phase to countries where travel data predict the pathogen will reach and to countries where lower healthcare resources will likely cause delays in reporting. Our results highlight the benefits, for all parties, when higher income countries provide additional healthcare resources for lower income countries, particularly those that have high air traffic volumes. In particular, international authorities should prioritize aid to those poorest countries where both the risk of emerging infectious diseases and air traffic volume is highest. This strategy will result in earlier detection of pathogens and a reduction in the impact of future pandemics.

Nature 546, 646–650 (2017); doi:10.1038/nature22975 In this Letter, owing to an error during the production process, Supplementary Tables 1–4 were missing from the HTML. These four tables have now been added to the HTML version of the original Letter.