Beyond Infection: Integrating Competence into Reservoir Host Prediction

Daniel J Becker; Stephanie N Seifert; Colin J Carlson

doi:10.1016/j.tree.2020.08.014

Beyond Infection: Integrating Competence into Reservoir Host Prediction

Trends Ecol Evol. 2020 Dec;35(12):1062-1065. doi: 10.1016/j.tree.2020.08.014. Epub 2020 Sep 11.

Authors

Daniel J Becker¹, Stephanie N Seifert², Colin J Carlson³

Affiliations

¹ Department of Biology, Indiana University, Bloomington, IN, USA. Electronic address: danbeck@iu.edu.
² Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, USA.
³ Center for Global Health Science and Security, Georgetown University Medical Center, Washington, D.C., USA.

Abstract

Most efforts to predict novel reservoirs of zoonotic pathogens use information about host exposure and infection rather than competence, defined as the ability to transmit pathogens. Better obtaining and integrating competence data into statistical models as covariates, as the response variable, and through postmodel validation should improve predictive research.

Keywords: SARS-CoV-2; machine learning; vector-borne disease; within-host; zoonoses.

Publication types

Research Support, N.I.H., Intramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Animals
Disease Reservoirs*
Zoonoses*