School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow G12 8QQ, United Kingdom; MRC-University of Glasgow Centre for Virus Research, G61 1QH, United Kingdom.
School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow G12 8QQ, United Kingdom; MRC-University of Glasgow Centre for Virus Research, G61 1QH, United Kingdom.
Curr Opin Virol. 2023 Aug;61:101346. doi: 10.1016/j.coviro.2023.101346. Epub 2023 Jul 27.
The prospect of identifying high-risk viruses and designing interventions to pre-empt their emergence into human populations is enticing, but controversial, particularly when used to justify large-scale virus discovery initiatives. We review the current state of these efforts, identifying three broad classes of predictive models that have differences in data inputs that define their potential utility for triaging newly discovered viruses for further investigation. Prospects for model predictions of public health risk to guide preparedness depend not only on computational improvements to algorithms, but also on more efficient data generation in laboratory, field and clinical settings. Beyond public health applications, efforts to predict zoonoses provide unique research value by creating generalisable understanding of the ecological and evolutionary factors that promote viral emergence.
识别高危病毒并设计干预措施以预先阻止其在人群中出现的前景令人向往，但颇具争议性，特别是当其被用于为大规模病毒发现计划辩护时。我们回顾了这些努力的现状，确定了三种广泛的预测模型类别，它们在数据输入方面存在差异，这决定了它们在为进一步调查新发现的病毒进行分类时的潜在效用。模型对公共卫生风险的预测结果是否能用于指导疾病防控准备工作，不仅取决于算法的计算改进，还取决于在实验室、现场和临床环境中更有效地生成数据。除了公共卫生应用之外，预测人畜共患病的努力还通过对促进病毒出现的生态和进化因素形成可推广的理解，提供了独特的研究价值。
