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Summary
Jan Brauner et al. and collaborators published Inferring the effectiveness of government interventions against COVID-19 in Science. The authors gathered chronological data on the implementation of NPIs (non-pharmaceutical interventions, i.e. policy or behavioral interventions) for 34 European, and 7 non-European countries between January and the end of May 2020. They estimated the effectiveness of a small number of NPIs, ranging from limiting gathering sizes, business closures, and closure of educational institutions to stay-at-home orders. They used a Bayesian hierarchical model that links NPI implementation dates to national case and death counts. Their results proved to be very robust, and they discovered that some NPIs outperformed others under all tested conditions. Closing schools and universities and limiting gatherings to 10 people or less are the NPIs with large effects, closing most non-essential businesses is the NPI with medium effects, whereas stay-at-home orders had a little additional effect. The results provided insight on the amount of COVID-19 transmission associated with various areas and activities of public life, such as gatherings of different sizes, and they might inform the packages of interventions that countries implement to control transmission in current and future waves of infections.
Reaction
This paper currently has the 8th highest attention score (measured by Altmetric) of all Science publications. This study leveraged data from multiple countries with diverse sets of interventions in place to disentangle the effects of individual NPIs and used a data-driven approach that allowed sidestepping assumptions about contact patterns and intensity, infectiousness of different age groups. The results in this paper are interesting, “closing most nonessential face-to-face businesses was only somewhat more effective than targeted closures, which only affected businesses with high infection risks, such as bars, restaurants, and nightclubs”, “issuing a stay-at-home order had a small effect when a country had already closed educational institutions, closed nonessential businesses and banned gatherings. However, this study is correlating data from January to May 2020, comparing the effect of closing all schools, including university, vs having all the schools open as they were at the beginning of the pandemic. And the study is a data mining study and as such, has limitations. There are potentially unobserved factors that are not accounted for. And this study cannot distinguish direct effects on transmission in schools and universities, which might confound their results. Also, the study has a bunch of countries in Europe and near Europe, but the author didn’t mention why they included these countries. Therefore, it’s a challenge to interpret these findings and using them to guide policy decisions.
Questions
- Are there any seasonal effects in these NPIs?
- Why they omit some policies such as mask-wearing effects?
- Is it still a useful study now?