What is it about?
In response to the outbreak of the coronavirus disease 2019 (Covid-19), governments worldwide have introduced multiple restriction policies, known as non-pharmaceutical interventions (NPIs). However, the impact of these control measures is still a topic of debate. In this study we analysed trends in the numbers of new cases and deaths to assess the extent to which human mobility and reproduction number (Rt) have been reduced. We have shown that all NPIs, except for mask wearing, significantly affected human mobility trends. From these, schools and cultural closure mandates showed the largest effect on social distancing. We also found that closing schools, issuing face mask usage, and work-from-home mandates also caused a persistent reduction on the Rt after their initiation, which was not observed with the other social distancing measures.
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Why is it important?
Assessing the effectiveness of NPIs to mitigate the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical to inform future preparedness response plans. However, rigorously studying the effectiveness of such interventions poses considerable methodological challenges. This work may provide insights into which control measures may be able to control the spread of SARS-CoV-2. However, because of the limitations inherent in observational study designs, our estimates should not be seen as final but rather as a contribution to a diverse body of evidence, alongside other retrospective studies, simulation studies, and experimental trials.
Perspectives
We hope this article provides the readers an understanding of the necessary assumptions needed to derive causal effect from observational data, the possible issues faced in causal analysis, and how to overcome them. We estimated the causal effect of interventions based on real-world retrospective data and we provide an interesting discussion on how to obtain such valid causal estimates. We hope you have fun reading it, as much as we had fun writing it.
Vesna Barros
IBM Research
Read the Original
This page is a summary of: A causal inference approach for estimating effects of non-pharmaceutical interventions during Covid-19 pandemic, PLoS ONE, September 2022, PLOS,
DOI: 10.1371/journal.pone.0265289.
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