Macro-Level Drivers of SARS-CoV-2 Transmission: A Data-Driven Analysis of Factors Contributing to Epidemic Growth During the First Wave of outbreaks in the United States

Abstract

Many questions remain unanswered about how SARS-CoV-2 transmission is influenced by aspects of the economy, environment, and health. A better understanding of how these factors interact can help us to design early health prevention and control strategies, and develop better predictive models for public health risk management of SARS-CoV-2. This study examines the associations between COVID-19 epidemic growth and macro-level determinants of transmission such as climate, socio-economic factors, demographic factors, and population health, during the first wave of outbreaks in the United States. A spatial-temporal data-set was created by collating information from a variety of data sources including the Johns Hopkins University's Centre for Systems Science and Engineering, the United States Census Bureau, the USDA Economic Research Service, the United States EPA, the National Climatic Data Center, the CDC and the Oxford COVID-19 Government Response Tracker (OxCGRT). A unique data-driven study design was implemented that allows us to assess the relationship between COVID-19 case and death epidemic doubling times and explanatory variables using a Generalized Additive Model (GAM). The main factors associated with case doubling times are higher population density, home overcrowding, manufacturing, and recreation industries. Poverty was also an important predictor of faster epidemic growth perhaps because of factors associated with in-work poverty-related conditions, although poverty is also a predictor of poor population health which is likely driving case and death reporting. Air pollution and diabetes were other important drivers of case reporting. Warmer temperatures are associated with slower epidemic growth, which is most likely explained by human behaviors associated with warmer locations i.e ventilating homes and workplaces. and socializing outdoors. The main factors associated with death doubling times were population density, poverty older age, diabetes, and air pollution. Temperature was also slightly significant slowing death doubling times. Such findings help underpin current understanding of the disease epidemiology and also support current policy and advice recommending ventilation of homes, work-spaces, and schools, along with social distancing and mask-wearing. The results also suggest that states which adopted more stringent containment measures early on did have some success suppressing the virus. We can presume that if this was replicated at a federal level, much better outcomes would have been observed across the United States.