This paper examines the different socio-economic determinants of the fatalities associated with the COVID-19 pandemic globally in social determinants of health frameworks. It adapts the Poisson pseudo-maximum-likelihood (PPML) and the quantile regression techniques to effectively exploit the non-linear estimates of the data in order to derive non-biased point estimates at each quantile and make interquantile comparisons. This is particularly useful in recommending which societal variables become most significant at catastrophic levels of a pandemic like COVID-19 when existing health systems become overwhelmed. These estimators are applied to panel data for 196 countries over days of infection from the first recorded case. The COVID-19-related data is from Our World in Data, and the socio-economic variables are from the World Bank’s World Development Indicators. The results establish that an improved adequate health infrastructure for both testing and treatment is necessary, but not sufficient. Health systems ultimately become overwhelmed and ineffective in managing cases and reducing mortality in the face of the rising pandemic. Complementary social, economic, physical and environmental factors are necessary for curbing deaths. These factors relate to improving the health stock of the population through reductions in both communicable and non-communicable comorbidities; enhancing sanitation and hygiene; and improving the nutrition of the population. Socio-economic and environmental measures are the reduction of household and ambient air pollution; reduction of exposure to alcohol and cigarettes; reduction of poverty and ensuring economic inclusion; and learning from the past to fine-tune governments’ control measures in order to minimize harm to the population while effectively curbing mortality.