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Sánchez, C. A., H. Li, K. L. Phelps, C. Zambrana-Torrelio, L.-F. Wang, P. Zhou, Z.-L. Shi, et al. 2022. A strategy to assess spillover risk of bat SARS-related coronaviruses in Southeast Asia. Nature Communications 13. https://doi.org/10.1038/s41467-022-31860-w
Emerging diseases caused by coronaviruses of likely bat origin (e.g., SARS, MERS, SADS, COVID-19) have disrupted global health and economies for two decades. Evidence suggests that some bat SARS-related coronaviruses (SARSr-CoVs) could infect people directly, and that their spillover is more frequent than previously recognized. Each zoonotic spillover of a novel virus represents an opportunity for evolutionary adaptation and further spread; therefore, quantifying the extent of this spillover may help target prevention programs. We derive current range distributions for known bat SARSr-CoV hosts and quantify their overlap with human populations. We then use probabilistic risk assessment and data on human-bat contact, human viral seroprevalence, and antibody duration to estimate that a median of 66,280 people (95% CI: 65,351–67,131) are infected with SARSr-CoVs annually in Southeast Asia. These data on the geography and scale of spillover can be used to target surveillance and prevention programs for potential future bat-CoV emergence. Coronaviruses may spill over from bats to humans. This study uses epidemiological data, species distribution models, and probabilistic risk assessment to map overlap among people and SARSr-CoV bat hosts and estimate how many people are infected with bat-origin SARSr-CoVs in Southeast Asia annually.
Cooper, N., A. L. Bond, J. L. Davis, R. Portela Miguez, L. Tomsett, and K. M. Helgen. 2019. Sex biases in bird and mammal natural history collections. Proceedings of the Royal Society B: Biological Sciences 286: 20192025. https://doi.org/10.1098/rspb.2019.2025
Natural history specimens are widely used across ecology, evolutionary biology and conservation. Although biological sex may influence all of these areas, it is often overlooked in large-scale studies using museum specimens. If collections are biased towards one sex, studies may not be representativ…
Zizka, A., A. Antonelli, and D. Silvestro. 2020. sampbias , a method for quantifying geographic sampling biases in species distribution data. Ecography 44: 25–32. https://doi.org/10.1111/ecog.05102
Geo‐referenced species occurrences from public databases have become essential to biodiversity research and conservation. However, geographical biases are widely recognized as a factor limiting the usefulness of such data for understanding species diversity and distribution. In particular, differenc…