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时间变化的病毒脱落在公共卫生环境监测建模中的作用:重新审视 2013 年以色列脊髓灰质炎病毒爆发。

The role of time-varying viral shedding in modelling environmental surveillance for public health: revisiting the 2013 poliovirus outbreak in Israel.

机构信息

Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA.

Central Virology Laboratory, Chaim Sheba Medical Center, Tel-Hashomer, Israel.

出版信息

J R Soc Interface. 2022 May;19(190):20220006. doi: 10.1098/rsif.2022.0006. Epub 2022 May 18.

DOI:10.1098/rsif.2022.0006
PMID:35582812
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9114981/
Abstract

Environmental pathogen surveillance is a sensitive tool that can detect early-stage outbreaks, and it is being used to track poliovirus and other pathogens. However, interpretation of longitudinal environmental surveillance signals is difficult because the relationship between infection incidence and viral load in wastewater depends on time-varying shedding intensity. We developed a mathematical model of time-varying poliovirus shedding intensity consistent with expert opinion across a range of immunization states. Incorporating this shedding model into an infectious disease transmission model, we analysed quantitative, polymerase chain reaction data from seven sites during the 2013 Israeli poliovirus outbreak. Compared to a constant shedding model, our time-varying shedding model estimated a slower peak (four weeks later), with more of the population reached by a vaccination campaign before infection and a lower cumulative incidence. We also estimated the population shed virus for an average of 29 days (95% CI 28-31), longer than expert opinion had suggested for a population that was purported to have received three or more inactivated polio vaccine (IPV) doses. One explanation is that IPV may not substantially affect shedding duration. Using realistic models of time-varying shedding coupled with longitudinal environmental surveillance may improve our understanding of outbreak dynamics of poliovirus, SARS-CoV-2, or other pathogens.

摘要

环境病原体监测是一种敏感的工具,可以检测早期爆发的情况,目前正在用于追踪脊髓灰质炎病毒和其他病原体。然而,由于废水中感染发生率与病毒载量之间的关系取决于随时间变化的脱落强度,因此对纵向环境监测信号的解释很困难。我们开发了一个与不同免疫状态下的专家意见一致的随时间变化的脊髓灰质炎病毒脱落强度的数学模型。将该脱落模型纳入传染病传播模型,我们分析了 2013 年以色列脊髓灰质炎病毒爆发期间七个地点的定量聚合酶链反应数据。与恒定脱落模型相比,我们的随时间变化的脱落模型估计的峰值出现时间更晚(晚四周),在感染之前,通过疫苗接种运动达到的人群更多,累积发病率更低。我们还估计人群平均脱落病毒 29 天(95%CI28-31),比专家意见认为接受三剂或更多灭活脊髓灰质炎疫苗(IPV)的人群脱落时间要长。一种解释是,IPV 可能不会显著影响脱落持续时间。使用随时间变化的脱落的现实模型并结合纵向环境监测,可能会提高我们对脊髓灰质炎病毒、SARS-CoV-2 或其他病原体爆发动态的理解。

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