Mitigation Interventions in the United States: An Exploratory Investigation of Determinants and Impacts

Abstract

Purpose: To examine the determinants and impacts of implementing the mitigation interventions to combat the COVID-19 disease in the United States during the first 5 weeks of the pandemic. Method: A content analysis identified nine types of mitigation interventions and the timing at which states enacted these strategies. A proportional hazard model, a multiple-event survival model, and a random-effect spatial error panel model in conjunction with a robust method analyzing zero-inflated and skewed outcomes were employed in the data analysis. Findings: Contradictory to the study hypothesis, states initially with a high COVID-19 prevalence rate enacted mitigation strategies slowly. Three mitigation strategies (nonessential business closure, large-gathering bans, and restaurant/bar limitations) showed positive impacts on reducing cumulative cases, new cases, and death rates across states. Conclusion: Some states may have missed optimal timing to implement mitigations. Swift implementation of mitigations is crucial. Reopening economy by fully lifting mitigation interventions is risky.

A Systematic Literature Review and Meta-Analysis of the Effects of Lockdowns on COVID-19 Mortality II

“…We report both of them and use the average in the precision-weighted average. ** The estimate from Guo et al (2021) is based on school closures in United States where all 50 states closed schools between March 13, 2020, andMarch 23, 2020, and44 states closed schools in just four school days (March 15, 2020 (Sunday) to March 19, 2020 (Friday)), see Table 1 in Auger et al (2020). Hence, the difference-in-difference effect is based on a maximum of seven school days.…”

“…Hence, the difference-in-difference effect is based on a maximum of seven school days. As noted by Chernozhukov et al (2021), this lack of cross-sectional variation can lead to considerable uncertainty over the effects of school closures, and the results from Guo et al (2021) should be treated with this in mind.…”

“…Several studies find no statistically significant effect of lockdowns on mortality. This includes Bjørnskov (2021a) and Goldstein et al (2021), who find no significant effect of stricter lockdowns (higher OxCGRT stringency index), Sears et al (2020) and Dave et al (2021), who find no significant effect of SIPOs, and An et al (2021) and Guo et al (2021) who find no significant negative (fewer deaths) effect of any of the analyzed NPIs, including business closures, school closures, and border closures.…”

“…The variety of definitions can be seen in the analysis of non-essential business closures and bar/restaurant closures. Chernozhukov et al (2021) focus on a combined parameter (the average of business closure and bar/restaurant closure in each state), Spiegel and Tookes (2021b) examine bar and/or restaurant closure but not business closure, and Guo et al (2021) look at both business closures and bar/restaurant closures independently.…”

Sustained Effects of Government Response on the COVID-19 Infection Rate in China: A Multiple Mediation Analysis

“…Take social distancing as an example. A series of studies found that isolating infected people decreased and delayed transmission as well as reducing the epidemic’s peak [ 4 , 8 , 18 , 19 , 20 ]. Using counterfactual simulations, another study discovered that if the same restrictions on mobility had been implemented just one to two weeks earlier, a substantial number of cases and deaths would have been avoided.…”