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. 2021 Jul 28;21(1):1472.
doi: 10.1186/s12889-021-11530-0.

Onset of effects of non-pharmaceutical interventions on COVID-19 infection rates in 176 countries

Ingo W Nader #  1 , Elisabeth L Zeilinger #  2 , Dana Jomar  1 , Clemens Zauchner  1
Affiliations

Onset of effects of non-pharmaceutical interventions on COVID-19 infection rates in 176 countries

Ingo W Nader et al. BMC Public Health. .

Abstract

Background: During 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.

Methods: We 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.

Results: Our 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.

Conclusions: Our results can inform political decisions regarding the choice of NPIs and how long they need to be in place to take effect.

Keywords: Accumulated local effect plots; COVID-19; Containment measures; Coronavirus; Crosscountry study; Government measures; Health policy; Infection rate; Machine learning; Mitigation measures; Non-pharmaceutical interventions.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Effects of NPIs as identified by the model. The panels of this figure show predicted changes in the growth rate given how long a certain NPI has been in place. These are the main effects (ALE plots) for the most important NPIs, as identified by the model. The dark brown line is the median effect over all bootstrap samples, grey lines depict individual bootstrap samples, and the light brown line represents the complete training set. The day of implementation is marked with a vertical, dashed line. Plots show a time frame of 2 weeks prior to the measure to 60 days after implementation
Fig. 2
Fig. 2
Time-related, NPI-independent effects as identified by the model. The panels show the predicted growth rate given the two time-related, but NPI-independent covariates that were used in the analysis. The panel on the left shows an absolute time scale, indicating the timing in the global COVID-19 outbreak (measured in relation to March 11, 2020, the day the WHO declared COVID-19 a pandemic). The panel on the right is a relative time scale within each country. It indicates how recent an outbreak is within that country (measured in relation to the day that 25 cumulative COVID-19 cases were reached). The dark brown line is the median effect over all bootstrap samples, grey lines depict individual bootstrap samples, and the light brown line represents the complete training set
Fig. 3
Fig. 3
Country-specific covariates and their effects as identified by the model. The panels show the predicted growth rate in relation the four country-specific covariates included in the model. The GDP at purchasing parity power (ppp) per capita was log-transformed to account for the high skewness. The dark brown line is the median effect over all bootstrap samples, grey lines depict individual bootstrap samples, and the light brown line represents the complete training set
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