Face masks considerably reduce COVID-19 cases in Germany.
Face masks considerably reduce COVID-19 cases in Germany.
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DOI：
10.1073/pnas.2015954117
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发表时间：
2020-12-22
影响因子：
11.1
通讯作者：
Wälde K
中科院分区：
文献类型：
--
作者：
Mitze T;Kosfeld R;Rode J;Wälde K
减缓 COVID-19 的传播是大多数政府的目标。了解各种公共卫生措施的有效性至关重要。我们研究口罩的有效性。我们使用有关德国报告的严重急性呼吸综合征冠状病毒 2 感染的公共区域数据。由于德国各地区在不同时间点强制佩戴口罩，我们可以比较戴口罩地区和不戴口罩地区的感染上升情况。权衡各种估计后，我们得出结论，强制佩戴口罩 20 天后，新增感染人数减少了约 45%。由于与其他公共卫生措施相比，经济成本接近于零，因此口罩似乎是对抗 COVID-19 的一种经济有效的手段。我们使用综合控制方法来分析口罩对德国 COVID-19 传播的影响。我们的识别方法利用了公共交通和商店强制佩戴口罩的时间点的区域差异。根据我们考虑的地区，我们发现强制引入口罩后 20 天内，新登记的严重急性呼吸综合征冠状病毒 2 感染人数减少了 15% 至 75%。评估各种估计的可信度后，我们得出的结论是，佩戴口罩可将报告感染的每日增长率降低约 47%。
Mitigating the spread of COVID-19 is the objective of most governments. It is of utmost importance to understand how effective various public health measures are. We study the effectiveness of face masks. We employ public regional data about reported severe acute respiratory syndrome coronavirus 2 infections for Germany. As face masks became mandatory at different points in time across German regions, we can compare the rise in infections in regions with masks and regions without masks. Weighing various estimates, we conclude that 20 d after becoming mandatory face masks have reduced the number of new infections by around 45%. As economic costs are close to zero compared to other public health measures, masks seem to be a cost-effective means to combat COVID-19. We use the synthetic control method to analyze the effect of face masks on the spread of COVID-19 in Germany. Our identification approach exploits regional variation in the point in time when wearing of face masks became mandatory in public transport and shops. Depending on the region we consider, we find that face masks reduced the number of newly registered severe acute respiratory syndrome coronavirus 2 infections between 15% and 75% over a period of 20 days after their mandatory introduction. Assessing the credibility of the various estimates, we conclude that face masks reduce the daily growth rate of reported infections by around 47%.
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影响因子：
2.1
作者：
Kreif, Noemi;Grieve, Richard;Hangartner, Dominik;Turner, Alex James;Nikolova, Silviya;Sutton, Matt
通讯作者：
Sutton, Matt
影响因子：
4.2
作者：
Abadie, Alberto;Diamond, Alexis;Hainmueller, Jens
通讯作者：
Hainmueller, Jens
DOI：
10.1073/pnas.2009637117
发表时间：
2020-06-30
影响因子：
11.1
作者：
Zhang, Renyi;Li, Yixin;Molina, Mario J.
通讯作者：
Molina, Mario J.
影响因子：
4.8
作者：
Galiani, Sebastian;Quistorff, Brian
通讯作者：
Quistorff, Brian
影响因子：
4.5
作者：
Kahler, Christian J.;Hain, Rainer
通讯作者：
Hain, Rainer