Face masks considerably reduce COVID-19 cases in Germany

Abstract

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%.

Early assessment of epidemiological trends associated with SARS-CoV-2 variants of concern in Germany

“…For our purpose of estimating the epidemiological effects of emerging VOC in German regions, we adopt and extend the data and estimation setup applied in Mitze et al (2020).…”

“…We further include time-constant cross-sectional predictors characterizing regional demographic structures and the regional health care system as in Mitze et al (2020) based on data from the INKAR online database of the Federal Institute for Research on Building, Urban Affairs and Spatial Development (INKAR, 2021). We use the latest year available in the database, which is 2017.…”

“…The objective of SCM is to compare the development of the 7-day incidence rate (SARS-CoV-2 infections per 100,000 population over the last seven days) after treatment start in the two sets of treated region(s) vis-à-vis a synthetic control groups selected from a donor pool of 197 comparison regions without any confirmed VOC case during the entire sample period December 15, 2020 to February 4, 2021. We compute daily treatment effects for a maximum of 31 days and express them as percentage difference to the last pre-treatment observation of the scaled outcome variable (to 100), see Figure 2 in the main text. We motivate the use of SCM as one element of our empirical identification strategy because the estimation approach has been shown to be a flexible and robust estimation tool that has previously been applied to COVID-related research, for instance, to study the effect of face masks on SAR-CoV-2 infection numbers in Germany (Mitze et al, 2020) and lockdown effectiveness for a counterfactual of Sweden (Cho, 2020) and the USA (Friedson et al, 2021). The key identification approach of SCM is to establish a counterfactual that mimics a situation in which the treatment in treated regions (here: the emergence of VOC cases) would not have taken place.…”

“…This ensures that pre-treatment differences in trends of the outcome variable are leveled. A formal description of the estimation approach and the underlying assumptions for effect identification are given in Abadie & Gardeazabal (2003); Abadie et al (2010); Cavallo et al (2013). For our purpose of estimating the epidemiological effects of emerging VOC in German regions, we adopt and extend the data and estimation setup applied in Mitze et al (2020).For both SCM applications, we set the start of the treatment period to January 5, 2021 and identify treatment effects of VOC throughout January and early February. In all four cases, the reporting of the first confirmed VOC case took place at least one week after the start of the treatment period (Cologne: January 12, 2021, Leverkusen: January 18, 2021, Düren: January 23, 2021, Flensburg: January 24, 2021).…”

“…We merge event data on confirmed VOC with daily SARS-CoV-2 infections (by the onset of symptoms) for each of the 401 NUTS-3 regions provided by Robert Koch Institute (2021b)and further regional characteristics as documented in Mitze et al (2020).We estimate the infection effects of VOC for two case studies (Flensburg and cluster of NRW cities) by means of the synthetic control method. We also compare all regions with at least one confirmed VOC case by means of difference-in-difference regressions and a complementary panel event study.…”