Social markers of a pandemic: modeling the association between cultural norms and COVID-19 spread data

Abstract

Abstract While cross-national differences of the epidemic curves of COVID-19 become evident, social markers of such variability are still unexplored. In order to investigate how certain social norms may underlie the heterogeneity of the spread of infections, global social data (including cultural values, indices of prosperity, and government effectiveness) and covariates (such as climate zone, economic indicator, and healthcare access and quality) of early transmission dynamics of COVID-19 were collected. Model-based clustering and random forest regression analysis were applied to identify distinct groups of societies and explore predictors of COVID-19 doubling time. Clustering revealed four groups: (1) reserved; (2) drifting; (3) assertive; and (4) compliant societies. Compliant societies from dry climate zones showed the highest doubling times in spite of increased population densities. Most relevant predictors of doubling time were population density, freedom of assembly and association, and agency, underlining the importance of social factors in the hetereogeneity of COVID-19 transmission rates. Our cluster typology might contribute to the explanation of cross-national variability in early transmission dynamics of highly infectious diseases.