Predicting zoonotic potential of viruses: where are we?
Predicting zoonotic potential of viruses: where are we?
复制标题
预测病毒的人畜共患病潜力：我们在哪里？
DOI：
10.1016/j.coviro.2023.101346
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发表时间：
2023-07-27
影响因子：
5.9
通讯作者：
Streicker, Daniel G.
中科院分区：
文献类型：
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作者：
Mollentze, Nardus;Streicker, Daniel G.
识别高风险病毒并设计干预措施以预先阻止其出现在人群中的前景是诱人的，但存在争议，特别是当用于证明大规模病毒发现计划的合理性时。我们回顾了这些努力的现状，确定了三大类预测模型，这些模型在数据输入方面存在差异，这些数据输入定义了它们对新发现的病毒进行进一步调查的潜在效用。公共卫生风险模型预测的前景，以指导准备不仅取决于算法的计算改进，而且在实验室，现场和临床环境中更有效的数据生成。除了公共卫生应用之外，预测人畜共患病的努力通过创造对促进病毒出现的生态和进化因素的普遍理解而提供了独特的研究价值。
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.