Effectiveness assessment of non-pharmaceutical interventions: lessons learned from the COVID-19 pandemic
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Effectiveness of non-pharmaceutical interventions (NPIs), such as school closures and stay-at-home orders, during the COVID-19 pandemic has been assessed in many studies. Such assessments can inform public health policies and contribute to evidence-based choices of NPIs during subsequent waves or future epidemics. However, methodological issues and no standardised assessment practices have restricted the practical value of the existing evidence. Here, we present and discuss lessons learned from the COVID-19 pandemic and make recommendations for standardising and improving assessment, data collection, and modelling. These recommendations could contribute to reliable and policy-relevant assessments of the effectiveness of NPIs during future epidemics.
| Original language | English |
|---|---|
| Journal | The Lancet Public Health |
| Volume | 8 |
| Issue number | 4 |
| Pages (from-to) | e311-e317 |
| Number of pages | 7 |
| ISSN | 2468-2667 |
| DOIs | |
| Publication status | Published - 2023 |
Bibliographical note
Funding Information:
NMF, HJTU, and SB acknowledge funding from the UK Research and Innovation Medical Research Council (Centre for Global Infectious Disease Analysis: MR/R015600/1). NMF and SB acknowledge funding from the National Institute for Health Research (NIHR) Health Protection Unit in Modelling and Health Economics (NIHR200908) and philanthropic funding from Community Jameel. SB received support from the Novo Nordisk Foundation via The Novo Nordisk Young Investigator Award (NNF20OC0059309), from the Danish National Research Foundation via a chair position, and from The Eric and Wendy Schmidt Fund For Strategic Innovation via the Schmidt Polymath Award (G-22-63345). MS and JB were funded by the Engineering and Physical Sciences Research Council Centre for Doctoral Training in Autonomous Intelligent Machines and Systems (EP/S024050/1). JB was supported by Cancer Research UK. TS received funding from ETH Zürich.
Publisher Copyright:
© 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
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