The majority of human emerging infectious diseases (EIDs) are zoonotic, with viruses originating in wild mammals of particular concern (e.g. HIV, Ebola, SARS)1–3. 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 likely harbor the next human virus, or which viruses can cross species boundaries5–7. Here we conduct the most comprehensive analysis yet 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 for the first time that bats harbor a significantly higher proportion of zoonotic viruses than all other mammalian orders. We 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.

In this paper, we discuss an extension to two popular approaches to modeling complex structures in ecological data: the generalized additive model (GAM) and the hierarchical model (HGLM). The hierarchical GAM (HGAM), allows modeling of nonlinear functional relationships between covariates and outcomes where the shape of the function itself varies between different grouping levels. We describe the theoretical connection between HGAMs, HGLMs, and GAMs, explain how to model different assumptions about the degree of intergroup variability in functional response, and show how HGAMs can be readily fitted using existing GAM software, the mgcv package in R. We also discuss computational and statistical issues with fitting these models, and demonstrate how to fit HGAMs on example data. All code and data used to generate this paper are available at: github.com/eric-pedersen/mixed-effect-gams.

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Bacillus anthracis is a spore-forming, Gram-positive bacterium responsible for anthrax, an acute infection that most significantly affects grazing livestock and wild ungulates, but also poses a threat to human health. The geographic extent of B. anthracis is poorly understood, despite multi-decade research on anthrax epizootic and epidemic dynamics; many countries have limited or inadequate surveillance systems, even within known endemic regions. Here, we compile a global occurrence dataset of human, livestock and wildlife anthrax outbreaks. With these records, we use boosted regression trees to produce a map of the global distribution of B. anthracis as a proxy for anthrax risk. We estimate that 1.83 billion people (95% credible interval (CI): 0.59-4.16 billion) live within regions of anthrax risk, but most of that population faces little occupational exposure. More informatively, a global total of 63.8 million poor livestock keepers (95% CI: 17.5-168.6 million) and 1.1 billion livestock (95% CI: 0.4-2.3 billion) live within vulnerable regions. Human and livestock vulnerability are both concentrated in rural rainfed systems throughout arid and temperate land across Eurasia, Africa and North America. We conclude by mapping where anthrax risk could disrupt sensitive conservation efforts for wild ungulates that coincide with anthrax-prone landscapes.

The realization that complex systems such as ecological communities can collapse or shift regimes suddenly and without rapid external forcing poses a serious challenge to our understanding and management of the natural world. The potential to identify early warning signals that would allow researchers and managers to predict such events before they happen has therefore been an invaluable discovery that offers a way forward in spite of such seemingly unpredictable behavior. Research into early warning signals has demonstrated that it is possible to define and detect such early warning signals in advance of a transition in certain contexts. Here we describe the pattern emerging as research continues to explore just how far we can generalize these results. A core of examples emerges that shares three properties: the phenomenon of rapid regime shifts, a pattern of 'critical slowing down' that can be used to detect the approaching shift, and a mechanism of bifurcation driving the sudden change. As research has expanded beyond these core examples, it is becoming clear that not all systems that show regime shifts exhibit critical slowing down, or vice versa. Even when systems exhibit critical slowing down, statistical detection is a challenge. We review the literature that explores these edge cases and highlight the need for (a) new early warning behaviors that can be used in cases where rapid shifts do not exhibit critical slowing down, (b) the development of methods to identify which behavior might be an appropriate signal when encountering a novel system; bearing in mind that a positive indication for some systems is a negative indication in others, and (c) statistical methods that can distinguish between signatures of early warning behaviors and noise.

Nipah virus (NiV) is an emerging bat-borne zoonotic virus that causes near-annual outbreaks of fatal encephalitis in South Asia—one of the most populous regions on Earth. In Bangladesh, infection occurs when people drink date-palm sap contaminated with bat excreta. Outbreaks are sporadic, and the influence of viral dynamics in bats on their temporal and spatial distribution is poorly understood. We analyzed data on host ecology, molecular epidemiology, serological dynamics, and viral genetics to characterize spatiotemporal patterns of NiV dynamics in its wildlife reservoir, Pteropus medius bats, in Bangladesh. We found that NiV transmission occurred throughout the country and throughout the year. Model results indicated that local transmission dynamics were modulated by density-dependent transmission, acquired immunity that is lost over time, and recrudescence. Increased transmission followed multiyear periods of declining seroprevalence due to bat-population turnover and individual loss of humoral immunity. Individual bats had smaller host ranges than other Pteropus species (spp.), although movement data and the discovery of a Malaysia-clade NiV strain in eastern Bangladesh suggest connectivity with bats east of Bangladesh. These data suggest that discrete multiannual local epizootics in bat populations contribute to the sporadic nature of NiV outbreaks in South Asia. At the same time, the broad spatial and temporal extent of NiV transmission, including the recent outbreak in Kerala, India, highlights the continued risk of spillover to humans wherever they may interact with pteropid bats and the importance of limiting opportunities for spillover throughout Pteropus’s range.

Human interaction with animals has been implicated as a primary risk factor for several high impact zoonoses, including many bat-origin viral diseases. However the animal-to-human spillover events that lead to emerging diseases are rarely observed or clinically examined, and the link between specific interactions and spillover risk is poorly understood. To investigate this phenomenon, we conducted biological-behavioral surveillance among rural residents in Yunnan, Guangxi, and Guangdong districts of Southern China, where we have identified a number of SARS-related coronaviruses in bats. Serum samples were tested for four bat-borne coronaviruses using newly developed enzyme-linked immunosorbent assays (ELISA). Survey data were used to characterize associations between human-animal contact and bat coronavirus spillover risk. A total of 1,596 residents were enrolled in the study from 2015 to 2017. Nine participants (0.6%) tested positive for bat coronaviruses. 265 (17%) participants reported severe acute respiratory infections (SARI) and/or influenza-like illness (ILI) symptoms in the past year, which were associated with poultry, carnivore, rodent/shrew, or bat contact, with variability by family income and district of residence. This study provides serological evidence of bat coronavirus spillover in rural communities in Southern China. The low seroprevalence observed in this study suggests that bat coronavirus spillover is a rare event. Nonetheless, this study highlights associations between human-animal interaction and zoonotic spillover risk. These findings can be used to support targeted biological behavioral surveillance in high-risk geographic areas in order to reduce the risk of zoonotic disease emergence.

14Between 10,000 and 600,000 species of mammal virus are estimated to have the 15 potential to spread in human populations, but the vast majority are currently cir-16 culating in wildlife, largely undescribed and undetected by disease outbreak surveil-17 lance 1,2,3 . In addition, changing climate and land use drive geographic range shifts 18 in wildlife, producing novel species assemblages and opportunities for viral sharing 19 between previously isolated species 4,5 . In some cases, this will inevitably facilitate 20 spillover into humans 6,7 -a possible mechanistic link between global environmental 21 change and emerging zoonotic disease 8 . Here, we map potential hotspots of viral 22 sharing, using a phylogeographic model of the mammal-virus network, and projec-23 tions of geographic range shifts for 3,870 mammal species under climate change and 24 land use scenarios for the year 2070. Shifting mammal species are predicted to ag-25 gregate at high elevations, in biodiversity hotspots, and in areas of high human pop-26 ulation density in Asia and Africa, sharing novel viruses between 3,000 and 13,000 27 times. Counter to expectations, holding warming under 2°C within the century 28 does not reduce new viral sharing, due to greater range expansions-highlighting 29 the need to invest in surveillance even in a low-warming future. Most projected vi-30 ral sharing is driven by diverse hyperreservoirs (rodents and bats) and large-bodied 31 predators (carnivores). Because of their unique dispersal capacity, bats account for 32 the majority of novel viral sharing, and are likely to share viruses along evolutionary 33 pathways that could facilitate future emergence in humans. Our findings highlight 34 the urgent need to pair viral surveillance and discovery efforts with biodiversity 35 surveys tracking range shifts, especially in tropical countries that harbor the most 36 emerging zoonoses. 37 2 Main Text 38In the face of rapid environmental change, survival for many species depends on moving 39 to track shifting climates. Even in a best case scenario, many species are projected 40 to shift a hundred kilometers or more in the next century 9,10 . In the process, many 41 animals will bring their parasites and pathogens into new environments 4,11 , creating new 42 evolutionary opportunities for host jumps 8 . Most conceptual frameworks for cross-species 43 transmission revolve around how these host jumps facilitate the spillover of new zoonotic 44 pathogens into humans 12,13,14 , but viral evolution is an undirected process 15 , in which 45 humans are only one of over 5,000 mammal species with over 12 million possible pairwise 46 combinations 16 . Despite their indisputable significance, zoonotic emergence events are 47 just the tip of the iceberg; almost all cross-species transmission events will occur among 48 wild mammals, largely undetected and mostly inconsequential for public health. 49Of the millions of possible pairwise viral exchanges, the vast majority are biologically 50 implausible, as host specie...