| Literature DB >> 34819645 |
Gregory F Albery1, Daniel J Becker2, Liam Brierley3, Cara E Brook4, Rebecca C Christofferson5, Lily E Cohen6, Tad A Dallas7, Evan A Eskew8, Anna Fagre9, Maxwell J Farrell10, Emma Glennon11, Sarah Guth4, Maxwell B Joseph12, Nardus Mollentze13,14, Benjamin A Neely15, Timothée Poisot16,17, Angela L Rasmussen18,19, Sadie J Ryan20,21,22, Stephanie Seifert23, Anna R Sjodin24, Erin M Sorrell25,26, Colin J Carlson27,28.
Abstract
Better methods to predict and prevent the emergence of zoonotic viruses could support future efforts to reduce the risk of epidemics. We propose a network science framework for understanding and predicting human and animal susceptibility to viral infections. Related approaches have so far helped to identify basic biological rules that govern cross-species transmission and structure the global virome. We highlight ways to make modelling both accurate and actionable, and discuss the barriers that prevent researchers from translating viral ecology into public health policies that could prevent future pandemics.Entities:
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Year: 2021 PMID: 34819645 DOI: 10.1038/s41564-021-00999-5
Source DB: PubMed Journal: Nat Microbiol ISSN: 2058-5276 Impact factor: 30.964