Abstract
Repeated emergence of zoonotic viruses from bat reservoirs into human populations demands predictive approaches to preemptively identify virus‐carrying bat species. Here, we use machine learning to examine drivers of viral diversity in bats, determine whether those drivers depend on viral genome type, and predict undetected viral carriers. Our results indicate that bat species with longer life spans, broad geographic distributions in the eastern hemisphere, and large group sizes carry more viruses overall. Life span was a stronger predictor of deoxyribonucleic acid viral diversity, while group size and family were more important for predicting ribonucleic acid viruses, potentially reflecting broad differences in infection duration. Importantly, our models predict 54 bat species as likely carriers of zoonotic viruses, despite not currently being considered reservoirs. Mapping these predictions as a proportion of local bat diversity, we identify global regions where efforts to reduce disease spillover into humans by identifying viral carriers may be most productive.
Highlights
Bats have been implicated in the transmission of a number of zoonotic diseases (e.g., SARS, Rabies, Nipah, Hendra) that, while often resulting in asymptomatic infections in bats, cause significant mortality in humans and domestic animals (Wang & Anderson, 2019)
Using a machine learning approach, we identify the most important bat traits for predicting the richness of viruses they carry and examine if and how the importance of these traits depends on a key viral trait—ribonucleic acid (RNA) versus deoxyribonucleic acid (DNA) genome
We confirm the importance of traits previously identified as predictors of viral diversity in small samplings of bat species or for single viral families, and highlight additional predictors of viral diversity
Summary
Bats have been implicated in the transmission of a number of zoonotic diseases (e.g., SARS, Rabies, Nipah, Hendra) that, while often resulting in asymptomatic infections in bats, cause significant mortality in humans and domestic animals (Wang & Anderson, 2019). While large group sizes are thought to promote viral transmission, leading to increased pathogen diversity, as observed elsewhere in the literature (reviewed in Patterson & Ruckstuhl, 2013) in bats there is evidence for both positive (Webber et al, 2017) and negative (Gay et al, 2014) associations with viral diversity This previous body of research has drawn conclusions from limited subsets of species (Webber et al, 2017, N = 51, ~4% of species; Luis et al, 2013, N = 66, ~5% of species; Turmell & Olival, 2009, N = 33, 3% of species) or restricted geographic areas (Gay et al, 2014, Southeast Asia, N = 20, ~2% of species; Maganga et al, 2014, Central and West Africa, N = 17, ~1% of species). We use our models to identify bat species that are likely—though currently undetected—carriers of viruses, highlighting species and geographic regions as key candidates for viral surveillance efforts
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