Optimising predictive models to prioritise viral discovery in zoonotic reservoirs

Becker, D. J. et al. (2022) Optimising predictive models to prioritise viral discovery in zoonotic reservoirs. Lancet Microbe, (doi: 10.1016/S2666-5247(21)00245-7) (PMID:35036970) (PMCID:PMC8747432)

Abstract

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.

• Item Type: Articles
• Additional Information: The Viral Emergence Research Initiative consortium is supported by L’Institut de Valorisation de Données through the Université de Montreal and by US National Science Foundation BII 2021909. LMB was supported by the Wellcome Trust (217221/Z/19/Z). MS was supported by the Research Foundation – Flanders (FWO17/PDO/067) and the Flemish Government under the Onderzoeksprogramma Artificiële Intelligentie Vlaanderen programme.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Bergner, Dr Laura and Farrell, Dr Maxwell
• Authors: Becker, D. J., Albery, G. F., Sjodin, A. R., Poisot, T., Bergner, L. M., Chen, B., Cohen, L. E., Dallas, T. A., Eskew, E. A., Fagre, A. C., Farrell, M. J., Guth, S., Han, B. A., Simmons, N. B., Stock, M., Teeling, E. C., and Carlson, C. J.
• College/School: College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
• Journal Name: Lancet Microbe
• Publisher: Elsevier
• ISSN: 2666-5247
• ISSN (Online): 2666-5247
• Published Online: 10 January 2022
• Copyright Holders: Copyright © 2022 Elsevier
• First Published: First published in Lancet Microbe 2022
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
307106		Epidemiology meets biotechnology: preventing viral emergence from bats	Daniel Streicker	Wellcome Trust (WELLCOTR)	217221/Z/19/Z	Institute of Biodiversity, Animal Health and Comparative Medicine