ABSTRACT
Studies examining factors responsible for COVID-19 incidence have mostly focused at the national or sub-national level. Here we undertake an analysis of COVID-19 cases at the global scale to identify key factors associated with disease incidence. A regression modeling framework was used to identify key variables associated with COVID-19 incidence, and to assess longitudinal trends in reported incidence at the country-level. New COVID-19 case dynamics in response to lockdowns was characterized via cluster analysis. Eleven variables were found to be independently associated with COVID-19 infections (p<1e-05) and a 4-variable model adequately explained global variations in COVID-19 cases (p<0.01). COVID-19 case trajectories for most countries followed the log-logistic curve. Six predominant country clusters summarized the differences in individual countrys response to lockdowns. Globally, economic and meteorological factors are important determinants of COVID-19 incidence. Analysis of longitudinal trends and lockdown effects on COVID-19 caseloads further highlights important nuances in country-specific responses to the pandemic. These findings on the first six months of the pandemic has important implications for additional phases of the disease currently underway in many countries.
