During the outbreak of the COVID-19 pandemic, Non-Pharmaceutical and Pharmaceutical treatments were alternative strategies for governments to intervene. Though many of these intervention methods proved to be effective to stop the spread of COVID-19, i.e., lockdown and curfew, they also posed risk to the economy; in such a scenario, an analysis on how to strike a balance becomes urgent. Our research leverages the mobility big data from the University of Maryland COVID-19 Impact Analysis Platform and employs the Generalized Additive Model (GAM), to understand how the social demographic variables, NPTs (Non-Pharmaceutical Treatments) and PTs (Pharmaceutical Treatments) affect the New Death Rate (NDR) at county-level. We also portray the mutual and interactive effects of NPTs and PTs on NDR. Our results show that there exists a specific usage rate of PTs where its marginal effect starts to suppress the NDR growth, and this specific rate can be reduced through implementing the NPTs.
【초록키워드】 COVID-19, Treatment, lockdown, COVID-19 pandemic, Economy, risk, Intervention, outbreak, Impact, Research, Model, university, Analysis, Demographic variables, balance, death rate, specific rate, Government, Additive Model, growth, pharmaceutical, Effect, Affect, effective, spread of COVID-19, Additive, Rate, reduced, suppress, New, Generalized, intervene, 【제목키워드】 Treatment, Intervention, empirical evidence,