Abstract
BackgroundDuring the initial phase of the global COVID-19 outbreak, most countries responded with non-pharmaceutical interventions (NPIs). In this study we investigate the general effectiveness of these NPIs, how long different NPIs need to be in place to take effect, and how long they should be in place for their maximum effect to unfold.MethodsWe used global data and a non-parametric machine learning model to estimate the effects of NPIs in relation to how long they have been in place. We applied a random forest model and used accumulated local effect (ALE) plots to derive estimates of the effectiveness of single NPIs in relation to their implementation date. In addition, we used bootstrap samples to investigate the variability in these ALE plots.ResultsOur results show that closure and regulation of schools was the most important NPI, associated with a pronounced effect about 10 days after implementation. Restrictions of mass gatherings and restrictions and regulations of businesses were found to have a more gradual effect, and social distancing was associated with a delayed effect starting about 18 days after implementation.ConclusionsOur results can inform political decisions regarding the choice of NPIs and how long they need to be in place to take effect.
Highlights
During the initial phase of the global COVID-19 outbreak, most countries responded with nonpharmaceutical interventions (NPIs)
Non-pharmaceutical interventions (NPIs) are applied by most countries around the world to reduce the risk of the COVID-19 pandemic and to slow the suspected exponential growth of infections
Because of the exponential growth during the onset of infectious diseases [12,13,14], and because we only consider the beginning of the outbreak in our analysis, the growth rate is expected to be constant when no NPIs are in place
Summary
During the initial phase of the global COVID-19 outbreak, most countries responded with nonpharmaceutical interventions (NPIs). Non-pharmaceutical interventions (NPIs) are applied by most countries around the world to reduce the risk of the COVID-19 pandemic and to slow the suspected exponential growth of infections. Since multiple aspects of the COVID-19 pandemic are unclear and complex, these simulation methods have to work with parameter assumptions based on fragmented knowledge [3]. Retrospective studies that only consider one country or region at a time in their analyses [8, 9], suffer from the problem that multiple NPIs are introduced simultaneously. They have no means to distinguish between effects of these interventions and can only evaluate a common effect.
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