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学校因流感集中关闭会削弱社会互动网络,从而减少流感传播。

Reactive school closure weakens the network of social interactions and reduces the spread of influenza.

机构信息

Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115.

ISI Foundation, 10126 Turin, Italy.

出版信息

Proc Natl Acad Sci U S A. 2019 Jul 2;116(27):13174-13181. doi: 10.1073/pnas.1821298116. Epub 2019 Jun 17.

Abstract

School-closure policies are considered one of the most promising nonpharmaceutical interventions for mitigating seasonal and pandemic influenza. However, their effectiveness is still debated, primarily due to the lack of empirical evidence about the behavior of the population during the implementation of the policy. Over the course of the 2015 to 2016 influenza season in Russia, we performed a diary-based contact survey to estimate the patterns of social interactions before and during the implementation of reactive school-closure strategies. We develop an innovative hybrid survey-modeling framework to estimate the time-varying network of human social interactions. By integrating this network with an infection transmission model, we reduce the uncertainty surrounding the impact of school-closure policies in mitigating the spread of influenza. When the school-closure policy is in place, we measure a significant reduction in the number of contacts made by students (14.2 vs. 6.5 contacts per day) and workers (11.2 vs. 8.7 contacts per day). This reduction is not offset by the measured increase in the number of contacts between students and nonhousehold relatives. Model simulations suggest that gradual reactive school-closure policies based on monitoring student absenteeism rates are capable of mitigating influenza spread. We estimate that without the implemented reactive strategies the attack rate of the 2015 to 2016 influenza season would have been 33% larger. Our study sheds light on the social mixing patterns of the population during the implementation of reactive school closures and provides key instruments for future cost-effectiveness analyses of school-closure policies.

摘要

学校关闭政策被认为是减轻季节性和大流行性流感的最有前途的非药物干预措施之一。然而,其有效性仍存在争议,主要是因为缺乏有关政策实施期间人群行为的经验证据。在 2015 年至 2016 年俄罗斯流感季节期间,我们进行了基于日记的接触调查,以估计在实施反应性学校关闭策略之前和期间的社会互动模式。我们开发了一种创新的混合调查-建模框架来估计人类社会互动的时变网络。通过将该网络与感染传播模型相结合,我们减少了围绕学校关闭政策在减轻流感传播方面的影响的不确定性。当学校关闭政策生效时,我们测量到学生(每天 14.2 次与 6.5 次接触)和工人(每天 11.2 次与 8.7 次接触)的接触次数明显减少。这种减少并没有被学生和非家庭成员之间接触次数的测量增加所抵消。模型模拟表明,基于监测学生缺勤率的渐进式反应性学校关闭政策能够减轻流感传播。我们估计,如果没有实施的反应性策略,2015 年至 2016 年流感季节的发病率将增加 33%。我们的研究揭示了在实施反应性学校关闭期间人群的社会混合模式,并为未来学校关闭政策的成本效益分析提供了关键工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ecb/6613079/1a956b54ed89/pnas.1821298116fig01.jpg

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