Optimising predictive models to prioritise viral discovery in zoonotic reservoirs

• Published in: The Lancet. Microbe Vol. 3; no. 8; pp. e625 - e637
• Main Authors: Becker, Daniel J, Albery, Gregory F, Sjodin, Anna R, Poisot, Timothée, Bergner, Laura M, Chen, Binqi, Cohen, Lily E, Dallas, Tad A, Eskew, Evan A, Fagre, Anna C, Farrell, Maxwell J, Guth, Sarah, Han, Barbara A, Simmons, Nancy B, Stock, Michiel, Teeling, Emma C, Carlson, Colin J
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.08.2022; The Authors. Published by Elsevier Ltd
• Subjects: Animals; Chiroptera; COVID-19 - epidemiology; Phylogeny; Review; SARS-CoV-2; Viruses
• ISSN: 2666-5247; 2666-5247
• DOI: 10.1016/S2666-5247(21)00245-7

Despite the global investment in One Health disease surveillance, it remains difficult and costly to identify and monitor the wildlife reservoirs of novel zoonotic viruses. Statistical models can guide sampling target prioritisation, but the predictions from any given model might be highly uncertain; moreover, systematic model validation is rare, and the drivers of model performance are consequently under-documented. Here, we use the bat hosts of betacoronaviruses as a case study for the data-driven process of comparing and validating predictive models of probable reservoir hosts. In early 2020, we generated an ensemble of eight statistical models that predicted host–virus associations and developed priority sampling recommendations for potential bat reservoirs of betacoronaviruses and bridge hosts for SARS-CoV-2. During a time frame of more than a year, we tracked the discovery of 47 new bat hosts of betacoronaviruses, validated the initial predictions, and dynamically updated our analytical pipeline. We found that ecological trait-based models performed well at predicting these novel hosts, whereas network methods consistently performed approximately as well or worse than expected at random. These findings illustrate the importance of ensemble modelling as a buffer against mixed-model quality and highlight the value of including host ecology in predictive models. Our revised models showed an improved performance compared with the initial ensemble, and predicted more than 400 bat species globally that could be undetected betacoronavirus hosts. We show, through systematic validation, that machine learning models can help to optimise wildlife sampling for undiscovered viruses and illustrates how such approaches are best implemented through a dynamic process of prediction, data collection, validation, and updating.