Assessing the effect of school closures on the spread of COVID-19 in Zurich

IF 1.5 3区数学 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS

Journal of the Royal Statistical Society Series A-Statistics in Society Pub Date : 2022-12-11 DOI:10.1111/rssa.12910

Maria Bekker-Nielsen Dunbar, Felix Hofmann, Leonhard Held, the SUSPend modelling consortium

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引用次数: 3

Abstract

The effect of school closure on the spread of COVID‐19 has been discussed intensively in the literature and the news. To capture the interdependencies between children and adults, we consider daily age‐stratified incidence data and contact patterns between age groups which change over time to reflect social distancing policy indicators. We fit a multivariate time‐series endemic–epidemic model to such data from the Canton of Zurich, Switzerland and use the model to predict the age‐specific incidence in a counterfactual approach (with and without school closures). The results indicate a 17% median increase of incidence in the youngest age group (0–14 year olds), whereas the relative increase in the other age groups drops to values between 2% and 3%. We argue that our approach is more informative to policy makers than summarising the effect of school closures with time‐dependent effective reproduction numbers, which are difficult to estimate due to the sparsity of incidence counts within the relevant age groups.

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评估苏黎世学校关闭对COVID-19传播的影响

关于学校停课对COVID-19传播的影响已经在文献和新闻中进行了深入讨论。为了捕捉儿童和成人之间的相互依赖关系，我们考虑了每日按年龄分层的发病率数据和年龄组之间的接触模式，这些数据随时间而变化，以反映社会距离政策指标。我们对来自瑞士苏黎世州的此类数据拟合了一个多变量时间序列流行病模型，并使用该模型以反事实方法预测特定年龄的发病率(无论是否有学校关闭)。结果表明，最年轻年龄组(0-14岁)的发病率中位数增加了17%，而其他年龄组的相对增幅降至2%至3%之间。我们认为，对于政策制定者来说，我们的方法比用与时间相关的有效再生产数字来总结学校关闭的影响更有意义。由于相关年龄组的发病率计数的稀疏性，有效再生产数字难以估计。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the Royal Statistical Society Series A-Statistics in Society 数学-统计学与概率论

CiteScore

2.90

自引率

5.00%

发文量

136

审稿时长

>12 weeks

期刊介绍： Series A (Statistics in Society) publishes high quality papers that demonstrate how statistical thinking, design and analyses play a vital role in all walks of life and benefit society in general. There is no restriction on subject-matter: any interesting, topical and revelatory applications of statistics are welcome. For example, important applications of statistical and related data science methodology in medicine, business and commerce, industry, economics and finance, education and teaching, physical and biomedical sciences, the environment, the law, government and politics, demography, psychology, sociology and sport all fall within the journal''s remit. The journal is therefore aimed at a wide statistical audience and at professional statisticians in particular. Its emphasis is on well-written and clearly reasoned quantitative approaches to problems in the real world rather than the exposition of technical detail. Thus, although the methodological basis of papers must be sound and adequately explained, methodology per se should not be the main focus of a Series A paper. Of particular interest are papers on topical or contentious statistical issues, papers which give reviews or exposés of current statistical concerns and papers which demonstrate how appropriate statistical thinking has contributed to our understanding of important substantive questions. Historical, professional and biographical contributions are also welcome, as are discussions of methods of data collection and of ethical issues, provided that all such papers have substantial statistical relevance.