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学校关闭对大流行性流感的影响:使用季节性 SIR 模型评估潜在影响。

The impact of school closures on pandemic influenza: Assessing potential repercussions using a seasonal SIR model.

机构信息

Department of Mathematics, Purdue University, West Lafayette, IN 47907, United States.

出版信息

Math Biosci Eng. 2012 Apr;9(2):413-30. doi: 10.3934/mbe.2012.9.413.

DOI:10.3934/mbe.2012.9.413
PMID:22901071
Abstract

When a new pandemic influenza strain has been identified, mass-production of vaccines can take several months, and antiviral drugs are expensive and usually in short supply. Social distancing measures, such as school closures, thus seem an attractive means to mitigate disease spread. However, the transmission of influenza is seasonal in nature, and as has been noted in previous studies, a decrease in the average transmission rate in a seasonal disease model may result in a larger final size. In the studies presented here, we analyze a hypothetical pandemic using a SIR epidemic model with time- and age-dependent transmission rates; using this model we assess and quantify, for the first time, the the effect of the timing and length of widespread school closures on influenza pandemic final size and average peak time. We find that the effect on pandemic progression strongly depends on the timing of the start of the school closure. For instance, we determine that school closures during a late spring wave of an epidemic can cause a pandemic to become up to 20% larger, but have the advantage that the average time of the peak is shifted by up to two months, possibly allowing enough time for development of vaccines to mitigate the larger size of the epidemic. Our studies thus suggest that when heterogeneity in transmission is a significant factor, decisions of public health policy will be particularly important as to how control measures such as school closures should be implemented.

摘要

当一种新的大流行性流感病毒株被确定后,疫苗的大规模生产可能需要几个月的时间,而抗病毒药物昂贵且通常供应短缺。因此,社交距离措施(例如关闭学校)似乎是减轻疾病传播的一种有吸引力的手段。然而,流感的传播具有季节性,正如之前的研究中所指出的,季节性疾病模型中平均传播率的降低可能导致最终规模更大。在本研究中,我们使用具有时间和年龄依赖性传播率的 SIR 传染病模型来分析假设的大流行;使用该模型,我们首次评估和量化了广泛的学校关闭的时间和持续时间对流感大流行最终规模和平均峰值时间的影响。我们发现,这种影响取决于学校关闭的时间。例如,我们确定在春季末的流行波中关闭学校可能会使大流行增加 20%,但它的优势在于,峰值的平均时间可以提前两个月,这可能为疫苗的开发留出足够的时间来减轻更大规模的流行。因此,我们的研究表明,当传播的异质性成为一个重要因素时,公共卫生政策的决策将特别重要,应如何实施学校关闭等控制措施。

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