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水库种群生态学、病毒进化与新发传染病风险。

Reservoir population ecology, viral evolution and the risk of emerging infectious disease.

机构信息

Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA.

Institute for Interdisciplinary Data Science, University of Idaho, Moscow, ID 83844, USA.

出版信息

Proc Biol Sci. 2022 Sep 14;289(1982):20221080. doi: 10.1098/rspb.2022.1080.

Abstract

The ecology and life history of wild animals influences their potential to harbour infectious disease. This observation has motivated studies identifying empirical relationships between traits of wild animals and historical patterns of spillover and emergence into humans. Although these studies have identified compelling broad-scale patterns, they are generally agnostic with respect to underlying mechanisms. Here, we develop mathematical models that couple reservoir population ecology with viral epidemiology and evolution to clarify existing verbal arguments and pinpoint the conditions that favour spillover and emergence. Our results support the idea that average lifespan influences the likelihood of an animal serving as a reservoir for human infectious disease. At the same time, however, our results show that the magnitude of this effect is sensitive to the rate of viral mutation. Our results also demonstrate that viral pathogens causing persistent infections or a transient immune response within the reservoir are more likely to fuel emergence. Genetically explicit stochastic simulations enrich these mathematical results by identifying relationships between the genetic basis of transmission and the risk of spillover and emergence. Together, our results clarify the scope of applicability for existing hypotheses and refine our understanding of emergence risk.

摘要

野生动物的生态和生活史影响其携带传染病的潜力。这一观察结果促使人们开展研究,以确定野生动物的特征与溢出和向人类出现的历史模式之间的经验关系。尽管这些研究已经确定了引人注目的大规模模式,但它们通常对潜在机制一无所知。在这里,我们开发了将水库种群生态学与病毒流行病学和进化相结合的数学模型,以澄清现有的口头论点,并指出有利于溢出和出现的条件。我们的结果支持这样一种观点,即平均寿命影响动物作为人类传染病储库的可能性。然而,与此同时,我们的结果表明,这种影响的程度对病毒突变率敏感。我们的结果还表明,在储层中引起持续感染或短暂免疫反应的病毒病原体更有可能引发出现。通过确定传播的遗传基础与溢出和出现的风险之间的关系,遗传明确的随机模拟丰富了这些数学结果。总之,我们的结果阐明了现有假设的适用范围,并深化了我们对出现风险的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7128/9470272/b5539ad92f64/rspb20221080f01.jpg

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