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The notion that certain animal groups disproportionately maintain and transmit viruses to humans due to broad-scale differences in ecology, life history, and physiology currently influences global health surveillance and research in disease ecology, virology, and immunology. To directly test whether such “special reservoirs” of zoonoses exist, we used literature searches to construct the largest existing dataset of virus–reservoir relationships, consisting of the avian and mammalian reservoir hosts of 415 RNA and DNA viruses along with their histories of human infection. Reservoir host effects on the propensity of viruses to have been reported as infecting humans were rare and when present were restricted to one or two viral families. The data instead support a largely host-neutral explanation for the distribution of human-infecting viruses across the animal orders studied. After controlling for higher baseline viral richness in mammals versus birds, the observed number of zoonoses per animal order increased as a function of their species richness. Animal orders of established importance as zoonotic reservoirs including bats and rodents were unexceptional, maintaining numbers of zoonoses that closely matched expectations for mammalian groups of their size. Our findings show that variation in the frequency of zoonoses among animal orders can be explained without invoking special ecological or immunological relationships between hosts and viruses, pointing to a need to reconsider current approaches aimed at finding and predicting novel zoonoses.

Determining which animal viruses may be capable of infecting humans is currently intractable at the time of their discovery, precluding prioritization of high-risk viruses for early investigation and outbreak preparedness. Given the increasing use of genomics in virus discovery and the otherwise sparse knowledge of the biology of newly discovered viruses, we developed machine learning models that identify candidate zoonoses solely using signatures of host range encoded in viral genomes. Within a dataset of 861 viral species with known zoonotic status, our approach outperformed models based on the phylogenetic relatedness of viruses to known human-infecting viruses (area under the receiver operating characteristic curve [AUC] = 0.773), distinguishing high-risk viruses within families that contain a minority of human-infecting species and identifying putatively undetected or so far unrealized zoonoses. Analyses of the underpinnings of model predictions suggested the existence of generalizable features of viral genomes that are independent of virus taxonomic relationships and that may preadapt viruses to infect humans. Our model reduced a second set of 645 animal-associated viruses that were excluded from training to 272 high and 41 very high-risk candidate zoonoses and showed significantly elevated predicted zoonotic risk in viruses from nonhuman primates, but not other mammalian or avian host groups. A second application showed that our models could have identified Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) as a relatively high-risk coronavirus strain and that this prediction required no prior knowledge of zoonotic Severe Acute Respiratory Syndrome (SARS)-related coronaviruses. Genome-based zoonotic risk assessment provides a rapid, low-cost approach to enable evidence-driven virus surveillance and increases the feasibility of downstream biological and ecological characterization of viruses.

Transmission of SARS-CoV-2 from humans to other species threatens wildlife conservation and may create novel sources of viral diversity for future zoonotic transmission. A variety of computational heuristics have been developed to pre-emptively identify susceptible host species based on variation in the angiotensin-converting enzyme 2 (ACE2) receptor used for viral entry. However, the predictive performance of these heuristics remains unknown. Using a newly compiled database of 96 species, we show that, while variation in ACE2 can be used by machine learning models to accurately predict animal susceptibility to sarbecoviruses (accuracy = 80.2%, binomial confidence interval [CI]: 70.8–87.6%), the sites informing predictions have no known involvement in virus binding and instead recapitulate host phylogeny. Models trained on host phylogeny alone performed equally well (accuracy = 84.4%, CI: 75.5–91.0%) and at a level equivalent to retrospective assessments of accuracy for previously published models. These results suggest that the predictive power of ACE2-based models derives from strong correlations with host phylogeny rather than processes which can be mechanistically linked to infection biology. Further, biased availability of ACE2 sequences misleads projections of the number and geographic distribution of at-risk species. Models based on host phylogeny reduce this bias, but identify a very large number of susceptible species, implying that model predictions must be combined with local knowledge of exposure risk to practically guide surveillance. Identifying barriers to viral infection or onward transmission beyond receptor binding and incorporating data which are independent of host phylogeny will be necessary to manage the ongoing risk of establishment of novel animal reservoirs of SARS-CoV-2. The COVID-19 pandemic affects humans, but also many of the animals we interact with. So far, humans have transmitted the SARS-CoV-2 virus to pet dogs and cats, a wide range of zoo animals, and even wildlife. Transmission of SARS-CoV-2 from humans to animals can lead to outbreaks amongst certain species, which can endanger animal populations and create new sources of human infections. Thus, careful monitoring of animal infections may help protect both animals and humans. Identifying which animals are susceptible to SARS-CoV-2 would help scientists monitor these species for outbreaks and viral circulation. Unfortunately, testing whether SARS-CoV-2 can infect different species in the laboratory is both time-consuming and expensive. To overcome this obstacle, researchers have used computational methods and existing data about the structure and genetic sequences of ACE2 receptors – the proteins on the cell surface that SARS-CoV-2 uses to enter the cell – to predict SARS-COV-2 susceptibility in different species. However, it remained unclear how accurate this approach was at predicting susceptibility in different animals, or whether their correct predictions indicated causal links between ACE2 variability and SARS-CoV-2 susceptibility. To assess the usefulness of this approach, Mollentze et al. started by using data on the ACE2 receptors from 96 different species and building a machine learning model to predict how susceptible those species might be to SARS-CoV-2. The susceptibility of these species had either been observed in natural infections – in zoos, for example – or had been assessed in the laboratory, so Mollentze et al. were able to use this information to determine how good both their model and previous approaches based on the sequence of ACE2 receptors were. The results showed that while the model was quite accurate (it correctly predicted susceptibility to SARS-CoV-2 about 80% of the time), its predictions were based on regions of the ACE2 receptors that were not known to interact with the virus. Instead, the regions that the machine learning model relied on were ones that tend to vary more the more distantly related two species are. This indicates that existing computational approaches are likely not relying on information about how ACE2 receptors interact with SARS-CoV-2 to predict susceptibility. Instead, they are simply using information on how closely related the different animal species are, which is much easier to source than data about ACE2 receptors. Indeed, the sequences of the ACE2 receptors in many species are unknown and the species for which this information is available come only from a few geographic areas. Mollentze et al. also showed that limiting the predictions about susceptibility to these species could mislead scientists when deciding which species and geographic areas to surveil for possible viral circulation. Instead, it may be more effective and cost-efficient to use animal relatedness to predict susceptibility to SARS-CoV-2. This makes it possible to make predictions for nearly all mammals, while being just as accurate as models based on ACE2 receptor data. However, Mollentze et al. point out that this approach would still fail to narrow down the number of animals that need to be monitored enough for it to be practical. Considering additional factors like how often the animals interact with humans or how prone they are to transmit the virus among themselves may help narrow it down more. Further research is therefore needed to identify the best multifactor approaches to identifying which animal populations should be monitored.

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

Rabies virus (RABV) host-shift events (HSEs) are thought to be promoted by viral genomic and ecological factors, but the relative balance of the two is unclear. Using a dataset of 19,170 species pairs that were known or not known to be linked by an HSE, we developed a logistic regression model to explore how biological and ecological characteristics of cross-species infections and their associated RABV variant (RVV) influence HSE risk. The model incorporates relatedness, body temperature, litter size, adult weight, and the broad lineage (bat or canine) of the RABV variant maintained by the reservoir species. Assessed with leave-one-out cross validation, the model identifies known HSEs with 90% accuracy (sensitivity 90%, specificity 82%). The susceptible-reservoir infection on each continent with the highest risk of HSE are coyotes with Canine-associated RVVs (North/Central America, RR = 187.0), culpeos with Canine-associated RVVs (South America, 100.9), dholes with Canine-associated RVVs (Asia, 159.8), arctic foxes with Raccoon Dog RVV (Europe, 115.8), and African wild dogs with Canine-associated RVVs (Africa, 134.8). The results of this model can be used to help predict the next HSE, identify potential cryptic RABV reservoirs, inform contingency actions when a high-risk event is identified, and prepare for importation or incursion events.

The contemporary surge in metagenomic sequencing has transformed knowledge of viral diversity in wildlife. However, evaluating which newly discovered viruses pose sufficient risk of infecting humans to merit detailed laboratory characterization and surveillance remains largely speculative. Machine learning algorithms have been developed to address this imbalance by ranking the relative likelihood of human infection based on viral genome sequences, but are not yet routinely applied to viruses at the time of their discovery. Here, we characterized viral genomes detected through metagenomic sequencing of feces and saliva from common vampire bats (Desmodus rotundus) and used these data as a case study in evaluating zoonotic potential using molecular sequencing data. Of 58 detected viral families, including 17 which infect mammals, the only known zoonosis detected was rabies virus; however, additional genomes were detected from the families Hepeviridae, Coronaviridae, Reoviridae, Astroviridae and Picornaviridae, all of which contain human-infecting species. In phylogenetic analyses, novel vampire bat viruses most frequently grouped with other bat viruses that are not currently known to infect humans. In agreement, machine learning models built from only phylogenetic information ranked all novel viruses similarly, yielding little insight into zoonotic potential. In contrast, genome composition-based machine learning models estimated different levels of zoonotic potential, even for closely related viruses, categorizing one out of four detected hepeviruses and two out of three picornaviruses as having high priority for further research. We highlight the value of evaluating zoonotic potential beyond ad hoc consideration of phylogeny and provide surveillance recommendations for novel viruses in a wildlife host which has frequent contact with humans and domestic animals.

Antiviral defenses can sense viral RNAs and mediate their destruction. This presents a challenge for host cells since they must destroy viral RNAs while sparing the host mRNAs that encode antiviral effectors. Here, we show that highly upregulated interferon-stimulated genes (ISGs), which encode antiviral proteins, have distinctive nucleotide compositions. We propose that self-targeting by antiviral effectors has selected for ISG transcripts that occupy a less self-targeted sequence space. Following interferon (IFN) stimulation, the CpG-targeting antiviral effector zinc-finger antiviral protein (ZAP) reduces the mRNA abundance of multiple host transcripts, providing a mechanistic explanation for the repression of many (but not all) interferon-repressed genes (IRGs). Notably, IRGs tend to be relatively CpG rich. In contrast, highly upregulated ISGs tend to be strongly CpG suppressed. Thus, ZAP is an example of an effector that has not only selected compositional biases in viral genomes but also appears to have notably shaped the composition of host transcripts in the vertebrate interferome.

HIV-1 transmission via sexual exposure is an inefficient process. When transmission does occur, newly infected individuals are colonized by the descendants of either a single virion or a very small number of establishing virions. These transmitted founder (TF) viruses are more interferon (IFN)-resistant than chronic control (CC) viruses present 6 months after transmission. To identify the specific molecular defences that make CC viruses more susceptible to the IFN-induced ‘antiviral state’, we established a single pair of fluorescent TF and CC viruses and used arrayed interferon-stimulated gene (ISG) expression screening to identify candidate antiviral effectors. However, we observed a relatively uniform ISG resistance of transmitted HIV-1, and this directed us to investigate possible underlying mechanisms. Simple simulations, where we varied a single parameter, illustrated that reduced growth rate could possibly underly apparent interferon sensitivity. To examine this possibility, we closely monitored in vitro propagation of a model TF/CC pair (closely matched in replicative fitness) over a targeted range of IFN concentrations. Fitting standard four-parameter logistic growth models, in which experimental variables were regressed against growth rate and carrying capacity, to our in vitro growth curves, further highlighted that small differences in replicative growth rates could recapitulate our in vitro observations. We reasoned that if growth rate underlies apparent interferon resistance, transmitted HIV-1 would be similarly resistant to any growth rate inhibitor. Accordingly, we show that two transmitted founder HIV-1 viruses are relatively resistant to antiretroviral drugs, while their matched chronic control viruses were more sensitive. We propose that, when present, the apparent IFN resistance of transmitted HIV-1 could possibly be explained by enhanced replicative fitness, as opposed to specific resistance to individual IFN-induced defences. However, further work is required to establish how generalisable this mechanism of relative IFN resistance might be.

Whether a pathogen entering a new host species results in a single infection or in onward transmission, and potentially an outbreak, depends upon the progression of infection in the index case. Although index infections are rarely observable in nature, experimental inoculations of pathogens into novel host species provide a rich and largely unexploited data source for meta-analyses to identify the host and pathogen determinants of variability in infection outcomes. We analyzed the progressions of 514 experimental cross-species inoculations of rabies virus, a widespread zoonosis which in nature exhibits both dead-end infections and varying levels of sustained transmission in novel hosts. Inoculations originating from bats rather than carnivores, and from warmer- to cooler-bodied species caused infections with shorter incubation periods that were associated with diminished virus excretion. Inoculations between distantly related hosts tended to result in shorter clinical disease periods, which are also expected to impede onward transmission. All effects were modulated by infection dose. Taken together, these results suggest that as host species become more dissimilar, increased virulence might act as a limiting factor preventing onward transmission. These results can explain observed constraints on rabies virus host shifts, describe a previously unrecognized role of host body temperature, and provide a potential explanation for host shifts being less likely between genetically distant species. More generally, our study highlights meta-analyses of experimental infections as a tractable approach to quantify the complex interactions between virus, reservoir, and novel host that shape the outcome of cross-species transmission.

Identifying virus characteristics associated with the largest public health impacts on human populations is critical to informing “zoonotic risk” assessments and surveillance strategies. Efforts to assess zoonotic risk often use trait-based analyses to identify which viral and reservoir host groups are most likely to source zoonoses but have not fully addressed how and why the impacts of zoonotic viruses vary in terms of disease severity (“virulence”), capacity to spread within human populations (“transmissibility”), or total human mortality (“death burden”). We analyzed trends in human case fatality rates, transmission capacities, and total death burdens across a comprehensive dataset of mammalian and avian zoonotic viruses. Bats harbor the most virulent zoonotic viruses even when compared to birds, which alongside bats have been hypothesized to be special zoonotic reservoirs due to molecular adaptations that support the physiology of flight. Reservoir host groups more closely related to humans—in particular, primates—harbor less virulent but more highly transmissible viruses. Importantly, a disproportionately high human death burden, arguably the most important metric of zoonotic risk, is not associated with any animal reservoir, including bats. Our data demonstrate that mechanisms driving death burdens are diverse and often contradict trait-based predictions. Ultimately, total human mortality is dependent on context-specific epidemiological dynamics, which are shaped by a combination of viral traits and conditions in the animal host population and across and beyond the human–animal interface. Understanding the conditions that predict high zoonotic burden in humans will require longitudinal studies of epidemiological dynamics in wildlife and human populations.