Comparing the impact on COVID-19 mortality of self-imposed behavior change and of government regulations across 13 countries

Abstract

Background: Countries have adopted different approaches, at different times, to reduce the transmission of coronavirus disease 2019 (COVID-19). Cross-country comparison could indicate the relative efficacy of these approaches. We assess various non-pharmaceutical interventions (NPIs) over time, comparing the effects of self-imposed (i.e. voluntary) behavior change and of changes enforced via official regulations, by statistically examining their impacts on subsequent death rates in 13 European countries. Methods and findings: We examine two types of NPI: the introduction of government-enforced closure policies over time; and self-imposed alteration of individual behaviors in response to awareness of the epidemic, in the period prior to regulations. Our proxy for the latter is Google mobility data, which captures voluntary behavior change when disease salience is sufficiently high. The primary outcome variable is the rate of change in COVID-19 fatalities per day, 16-20 days after interventions take place. Linear multivariate regression analysis is used to evaluate impacts. Voluntarily reduced mobility, occurring prior to government policies, decreases the percent change in deaths per day by 9.2 percentage points (95% CI 4.5-14.0 pp). Government closure policies decrease the percent change in deaths per day by 14.0 percentage points (95% CI 10.8-17.2 pp). Disaggregating government policies, the most beneficial are intercity travel restrictions, cancelling public events, and closing non-essential workplaces. Other sub-components, such as closing schools and imposing stay-at-home rules, show smaller and statistically insignificant impacts. Conclusions: This study shows that NPIs have substantially reduced fatalities arising from COVID-19. Importantly, the effect of voluntary behavior change is of the same order of magnitude as government-mandated regulations. These findings, including the substantial variation across dimensions of closure, have implications for the phased withdrawal of government policies as the epidemic recedes, and for the possible reimposition of regulations if a second wave occurs, especially given the substantial economic and human welfare consequences of maintaining lockdowns.

Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study

“…The global Coronavirus Disease 2019 (COVID-19) pandemic affected millions of people and forced the mobilization of governments worldwide. New regulations were adopted around the globe, and responses from citizens to novel measures were diverse [1]. Importantly, individual responses are critical in shaping the course of the current pandemic and of comparable health crises, since modern-day human behavior greatly influences the propagation and extinction of diseases (e.g., by following hygiene recommendations, stay at home orders, practicing physical distancing, and achieving immunity through vaccinations) [2].…”

Do school closures and school reopenings affect community transmission of COVID-19? A systematic review of observational studies

“…the unit of analysis was national or regional. Of the school closure studies, 13 reported data from a single country or region (the USA (n=10)(14,19–21,33,37,42,47–49), Italy (n=1)(23), Japan (n=1)(29), and Switzerland (n=1)(43)); four reported discrete estimates for several countries(26,38,44,53); and 15 studies pooled data from multiple countries (globally (n=8)(31,34– 36,39,46,50,51), Europe only (n=2)(24, 30), Europe and other high income countries (n=5)(15,18,32,40,52)). The studies on school reopening generally reported on single countries (Germany (n=2)(22, 28), USA (n=1)(25), Switzerland (n=1)(43), Belgium (n=1)(27), Israel (n=1)(45), Italy (n=1)(23)), but one reported discrete estimates for three countries (Denamrk, Germany and Norway)(44), two pooled data from multiple countries globally(16, 35), and one pooled data from multiple European countries(24).…”

“…The studies utilised different analytic approaches: regression models (n=24)(14,17,19– 21,25,26,28,30,31,33,35,36,39–42,44,46,48,49,51–53), Bayesian modelling (n=3)(15,18,47), comparison to a synthetic control group (n=4)(24,34,38,44), machine learning approaches (n=2)(43, 50), time series analysis (n=1)(29), and visual representation of changes in transmission over time compared against the timing of school policy interventions, with or without formal statistical analysis (n=4)(16,22,37,45). We identified three study designs used to estimate the effect of school closures: pooled multiple-area before-after comparisons (n=22)(14,15,18–21,24,26,30,32–36,39,40,42,46–50), within-area before-after comparisons(n=7)(23,29,37,38,43,44,53), and pooled multiple-area comparisons of interventions in place at a fixed time point (n=3)(31,51,52).…”

“…Among school closure studies, 18 studies(14,15,19,20,24,26,29,31–34,37,39,42–44,50,51) reported effects on incidence, 11(14,19,21,30,38–40,42,46,52,53) on mortality, one(37) on hospital admissioins and mortality, and eight(18,21,23,35,36,43,47,48) on an estimate of the effective Reproductive number (R) (derived from incidence and/or mortality data). Of the school reopening studies, six reported effects on incidence(16,22,24,28,44,45), two on hospitalisations(25, 44), and four on R(23,27,35,43).…”

Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study.

“…The global Corona Virus Disease 2019 pandemic affected millions of people and forced the mobilization of governments worldwide. New regulations were adopted around the globe, and citizens' responses to novel measures were diverse (Jamison, Bundy, Jamison, Spitz, & Verguet, 2020).…”