Introduction

First reports of a pneumonia of unknown etiology emerged in Wuhan, China, on December 31, 2019. The extremely contagious virus was identified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and spread quickly beyond Wuhan. In the U.S., the first case of COVID-19, the disease caused by SARS-CoV-2, was reported on January 22, 2020. Despite unprecedented government action, the number of cases in the U.S. grew to over 593000 as of April 14, and crossed one million on April 28.1
The local press and epidemiological research alike have reported local differences in the outbreak.2 A community’s susceptibility to any virus is determined by a variety of factors, inter alia, biological determinants, demographic profiles, and socioeconomic characteristics.3 These factors vary significantly across the U.S.; for instance, COVID-19 fatalities in New York, an epicenter of the outbreak in the U.S., disproportionally affect males and people belonging to older age groups, from Black/African and Hispanic ethnicities, and with certain comorbidities.4
As of May 09, 2020, more than half of COVID-19 data reported by the Centers for Disease Control and Prevention (CDC) are missing race and ethnicity disaggregation; other individual variables are lacking as well. To understand local differences in the outbreak and risk of contracting COVID-19, we therefore deploy an ecological analysis using contextual factors. A two-level hierarchical linear model with full maximum likelihood allows us to simultaneously test and disentangle county- and state-level effects.
Our study contributes to various strands of current COVID-19 research. First, we note that contextual factors influence the COVID-19 outbreak. Because significant variations in the outbreak exist between states and counties within a state (Figures 1 and 2),2 we recommend policy makers to look at pandemics from the smallest subdivision possible for effective implementation of countermeasures and provision of critical resources. Second, we develop an understanding of how regional cultural differences relate to outbreak variations, driven by specific psychological functioning of individuals and the enduring effects of such differences on political processes, governmental institutions, and public policies.5,6 Third, we debunk rumors that a state’s leadership, as expressed by the political party in control or the gender of its governor, has a statistically significant influence on the outbreak.7 Fourth, we identify how the virus affects counties differently, depending on their demographic profile. Fifth, while good personal health is generally associated with a lower risk, we identify the prevalence of obesity and smoking in counties to be negatively related with the outbreak. Sixth, while previous studies link air pollution to the death rate, we show that it also contributes to the number of cases.