Social, economic, and environmental factors influencing the basic reproduction number of COVID-19 across countries

Objective To assess whether the basic reproduction number (R-0) of COVID-19 is different across countries and what national-level demographic, social, and environmental factors other than interventions characterize initial vulnerability to the virus. Methods We fit logistic growth curves to reported daily case numbers, up to the first epidemic peak, for 58 countries for which 16 explanatory covariates are available. This fitting has been shown to robustly estimate R-0 from the specified period. We then use a generalized additive model (GAM) to discern both linear and nonlinear effects, and include 5 random effect covariates to account for potential differences in testing and reporting that can bias the estimated R-0. Findings We found that the mean R0 is 1.70 (S.D. 0.57), with a range between 1.10 (Ghana) and 3.52 (South Korea). We identified four factors-population between 20-34 years old (youth), population residing in urban agglomerates over 1 million (city), social media use to organize offline action (social media), and GINI income inequality-as having strong relationships with R-0, across countries. An intermediate level of youth and GINI inequality are associated with high R-0, (n-shape relationships), while high city population and high social media use are associated with high R-0. Pollution, temperature, and humidity did not have strong relationships with R-0 but were positive. Conclusion Countries have different characteristics that predispose them to greater intrinsic vulnerability to COVID-19. Studies that aim to measure the effectiveness of interventions across locations should account for these baseline differences in social and demographic characteristics.

Automated summary

By analyzing data from 58 countries, the study found significant differences in R0 of COVID-19 across different countries, associated with factors such as youth population, urban population, social media use, and income inequality. These results indicate that countries have different characteristics that predispose them to greater vulnerability to COVID-19.