An Exploratory Investigation of the Impacts of the Mitigation Strategies in the First Five Weeks of the Pandemic

Backgrounds and Purpose: In order to control the spread of COVID-19, unprecedented implementation of social distancing and other mitigation interventions were implemented, with an estimated economic loss of at least 2 trillion U.S. dollars. Inevitably, policy makers need to achieve trade-off between economic development and public health concerns. Evaluating the impacts of mitigation interventions is essentially a task of policy evaluation. This study is to explore the effectiveness of nine identified social distancing interventions that governors enacted to reduce the spread of Covid-19.

Methods: To explore the impacts of mitigation interventions, we conducted statistical analyses that link the timing of each mitigation order to various case counts of Covid-19. The outcome data come from Johns Hopkins University Coronavirus Data Stream that combines WHO and CDC case data. Based on the original input data, we employed 9 variables in the investigation: cumulative cases, cumulative deaths, new cases, new deaths, cumulative cases per 10,000 population, cumulative deaths per 10,000 population, cumulative new cases per 10,000 population, cumulative new deaths per 10,000 population, and death rate defined as the number of cumulative deaths divided by the cumulative cases. Efforts were made to address complicated data issues. First, we employed an inverse-normal transformation (INT) to handle the zero-inflated and skewed distribution of the outcome data. Second, spatial autocorrelations were present in the model since the coefficient of spatial error autocorrelation was statistically significant in 6 of them (p<.05). Finally, to correct for both temporal and spatial autocorrelations, we employed a random-effects spatial error panel model in conjunction with indirect-INT in the final analysis.

Findings: Results show that of 72 regression coefficients (i.e., 8 mitigation measures by 9 outcome variables), only five were significantly related to mitigation strategies (p<.05, one-tailed test). All other coefficients are not statistically significant. The following five coefficients suggest that mitigation interventions are effective in reducing Covid-19 case data. That is, other things being equal, enacting a non-essential business ban reduces 0.228 daily cumulative cases, reduces 0.133 daily new cases, and reduces 0.134 daily cumulative cases per 10,000 population; enacting a large gathering ban reduces the daily Covid-19 death rate by 35.5%; and enacting a restaurant/bar limit reduces the daily Covid-19 death rate by 22.3%.

Conclusions and Implications: In summary, three mitigation strategies (i.e., non-essential business ban, large gathering ban of more than 10 people, and restaurant/bar limit) may be effective in reducing cumulative cases, new cases, and death rate. All three strategies aim to increase social distance by reducing social gathering and contact. Other social distancing strategies were not correlated with outcomes, and it is unknown whether effects would be observed over a greater intervention period.