ABSTRACT
Some countries have been crippled by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic while others have emerged with few infections and fatalities; the factors underscoring this macro-epidemiological variation is one of the mysteries of this global catastrophe. Variation in immune responses influence SARS-CoV-2 transmission and mortality, and factors shaping this variation at the country level, in addition to other socio-ecological drivers, may be important. Here, we construct spatially explicit Bayesian models that combine data on prevalence of endemic diseases and other socio-ecological characteristics to quantify patterns of confirmed deaths and cases across the globe before mass vaccination. We find that the prevalence of parasitic worms, human immunodeficiency virus and malaria play a surprisingly important role in predicting country-level SARS-CoV-2 patterns. When combined with factors such as population density, our models predict 63% (56-67) and 76% (69-81) of confirmed cases and deaths among countries, respectively. While our findings at this macro-scale are necessarily associative, they highlight a need for studies to consider factors, such as infection by other pathogens, on global SARS-CoV-2 dynamics. These relationships are vital for developing countries that already have the highest burden of endemic disease and are becoming the most affected by the SARS-CoV-2 pandemic.