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Optimizing predictive models to prioritize viral discovery in zoonotic reservoirs

View ORCID ProfileDaniel J. Becker, View ORCID ProfileGregory F. Albery, View ORCID ProfileAnna R. Sjodin, View ORCID ProfileTimothée Poisot, View ORCID ProfileLaura M. Bergner, View ORCID ProfileTad A. Dallas, View ORCID ProfileEvan A. Eskew, View ORCID ProfileMaxwell J. Farrell, View ORCID ProfileSarah Guth, View ORCID ProfileBarbara A. Han, View ORCID ProfileNancy B. Simmons, View ORCID ProfileMichiel Stock, Emma C. Teeling, View ORCID ProfileColin J. Carlson
doi: https://doi.org/10.1101/2020.05.22.111344
Daniel J. Becker
1Department of Biology, University of Oklahoma, Norman, OK, U.S.A.
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Gregory F. Albery
2Department of Biology, Georgetown University, Washington, D.C., U.S.A.
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Anna R. Sjodin
3Department of Biological Sciences, University of Idaho, Moscow, ID, U.S.A.
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Timothée Poisot
4Université de Montréal, Département de Sciences Biologiques, Montréal, QC, Canada
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Laura M. Bergner
5Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, U.K.
6MRC–University of Glasgow Centre for Virus Research, Glasgow, U.K.
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Tad A. Dallas
7Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, U.S.A.
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Evan A. Eskew
8Department of Biology, Pacific Lutheran University, Tacoma, WA, U.S.A.
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Maxwell J. Farrell
9Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, ON, Canada
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Sarah Guth
10Department of Integrative Biology, University of California Berkeley, Berkeley, CA, U.S.A.
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Barbara A. Han
11Cary Institute of Ecosystem Studies, Millbrook, NY, U.S.A.
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Nancy B. Simmons
12Department of Mammalogy, Division of Vertebrate Zoology, American Museum of Natural History, New York, NY, U.S.A.
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Michiel Stock
13KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Belgium
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Emma C. Teeling
14School of Biology and Environmental Science, Science Centre West, University College Dublin, Belfield, Dublin, Ireland
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Colin J. Carlson
2Department of Biology, Georgetown University, Washington, D.C., U.S.A.
15Center for Global Health Science and Security, Georgetown University Medical Center, Washington, D.C., U.S.A.
16Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, D.C., U.S.A.
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  • ORCID record for Colin J. Carlson
  • For correspondence: colin.carlson@georgetown.edu
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Abstract

Despite global investment in One Health disease surveillance, it remains difficult—and often very costly—to identify and monitor the wildlife reservoirs of novel zoonotic viruses. Statistical models can be used to guide sampling prioritization, but predictions from any given model may be highly uncertain; moreover, systematic model validation is rare, and the drivers of model performance are consequently under-documented. Here, we use bat hosts of betacoronaviruses as a case study for the data-driven process of comparing and validating predictive models of likely reservoir hosts. In the first quarter of 2020, we generated an ensemble of eight statistical models that predict host-virus associations and developed priority sampling recommendations for potential bat reservoirs and potential bridge hosts for SARS-CoV-2. Over more than a year, we tracked the discovery of 40 new bat hosts of betacoronaviruses, validated initial predictions, and dynamically updated our analytic pipeline. We find that ecological trait-based models perform extremely well at predicting these novel hosts, whereas network methods consistently perform roughly as well or worse than expected at random. These findings illustrate the importance of ensembling as a buffer against variation in model quality and highlight the value of including host ecology in predictive models. Our revised models show improved performance and predict over 400 bat species globally that could be undetected hosts of betacoronaviruses. Although 20 species of horseshoe bats (Rhinolophus spp.) are known to be the primary reservoir of SARS-like viruses, we find at least three-fourths of plausible betacoronavirus reservoirs in this bat genus might still be undetected. Our study is the first to demonstrate through systematic validation that machine learning models can help optimize wildlife sampling for undiscovered viruses and illustrates how such approaches are best implemented through a dynamic process of prediction, data collection, validation, and updating.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵⍰ These authors share lead author status

  • Revision

  • http://www.viralemergence.org/betacov

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Optimizing predictive models to prioritize viral discovery in zoonotic reservoirs
Daniel J. Becker, Gregory F. Albery, Anna R. Sjodin, Timothée Poisot, Laura M. Bergner, Tad A. Dallas, Evan A. Eskew, Maxwell J. Farrell, Sarah Guth, Barbara A. Han, Nancy B. Simmons, Michiel Stock, Emma C. Teeling, Colin J. Carlson
bioRxiv 2020.05.22.111344; doi: https://doi.org/10.1101/2020.05.22.111344
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Optimizing predictive models to prioritize viral discovery in zoonotic reservoirs
Daniel J. Becker, Gregory F. Albery, Anna R. Sjodin, Timothée Poisot, Laura M. Bergner, Tad A. Dallas, Evan A. Eskew, Maxwell J. Farrell, Sarah Guth, Barbara A. Han, Nancy B. Simmons, Michiel Stock, Emma C. Teeling, Colin J. Carlson
bioRxiv 2020.05.22.111344; doi: https://doi.org/10.1101/2020.05.22.111344

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