Intelligently predicting viral spillover risks from bats and other wild mammals

PROJECT SUMMARY The transmission or ‘spillover’ of wildlife viruses to humans is a critical threat to global health, with outbreaks of viral pathogens like filoviruses, paramyxoviruses, and coronaviruses all originating in wild mammals. A key outstanding question is whether specific taxonomic groups, such as bats, warrant extra surveillance as ‘special reservoirs’ of viruses that are potentially pathogenic to humans. However, existing host-virus datasets are not sufficiently resolved to predict fine-grain risk for species or genera. An effective response must therefore address two core aims: (i) synthesizing knowledge regarding virus-to-mammal interactions; and (ii) using that knowledgebase to robustly predict future spillover events (i.e., zoonotic risk). To enable robust analysis and reusability of public datasets of NIAID’s Bioinformatics Resource Center (BRC; especially NCBI Virus and Virus Pathogen Resources, ViPR), the project will develop Host-Virus Data Intelligence to address three main problems for data reuse: confidence of the taxonomic assignments of mammals and viruses in observations; confidence in the evidence for proposed mammal-virus interactions; and connecting all the relevant data in published texts that are hidden from existing databases. The project team will construct a novel bioinformatic pipeline that will digitally connect taxonomic knowledge, use it to search dark data to find evidence of potential host-virus interactions, and then link it together using metadata layers (‘data about the data’) to form a more expansive host-virus knowledge graph than previously feasible. The project’s computational approach leverages information extraction methods in natural language processing as well as novel applications of artificial intelligence methods such as probabilistic inductive logic programming. A key anticipated outcome is to expand the dataset of host-virus interactions by 3-fold compared to comprehensive existing datasets. The proposed project will lay the foundation for a new generation of work reusing host-virus interaction data to test previously inaccessible hypotheses about how species’ traits impact viral spillover to humans. Shifting the paradigm to graph-based analyses, compared to purely taxonomic representations of host-virus interactions, will allow researchers to directly investigate the impact of ecosystem structure and human encroachment upon viral loads. Determining whether all mammals have equal risk of viral spillover, or whether some groups have higher taxon-specific zoonotic risk (e.g., horseshoe bats, murid rodents), is critical information for public health workers and epidemiologists. More definitive risk quantification will also help researchers identify which ecophysiological adaptations predispose certain groups to tolerating more viruses, which may in turn lead to clinical treatments by modeling the immune responses of wild mammals. Filling the identified gaps in host-virus knowledge is therefore essential to aid the progress of zoonotic disease research in the wake of COVID-19.