人病毒体
大流行
寄主(生物学)
生物
公共卫生
计算生物学
人类健康
数据科学
2019年冠状病毒病(COVID-19)
风险分析(工程)
基因组
计算机科学
生态学
传染病(医学专业)
疾病
环境卫生
医学
基因
遗传学
病理
护理部
作者
Gregory F. Albery,Daniel J. Becker,Liam Brierley,Cara E. Brook,Rebecca C. Christofferson,Lily E. Cohen,Tad Dallas,Evan A. Eskew,Anna C. Fagre,Maxwell J. Farrell,Emma E. Glennon,Sarah Guth,Maxwell B. Joseph,Nardus Mollentze,Benjamin A. Neely,Timothée Poisot,Angela L. Rasmussen,Sadie J. Ryan,Stephanie N. Seifert,Anna Sjödin
标识
DOI:10.1038/s41564-021-00999-5
摘要
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. A network science framework for understanding and predicting human and animal susceptibility to viral infections is proposed.
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