A Comprehensive Analysis of COVID-19 Transmission and Fatality Rates at the County level in the United States considering Socio-Demographics, Health Indicators, Mobility Trends and Health Care Infrastructure Attributes

SUMMARY Background Several research efforts have evaluated the impact of various factors including a) socio-demographics, (b) health indicators, (c) mobility trends, and (d) health care infrastructure attributes on COVID-19 transmission and mortality rate. However, earlier research focused only on a subset of variable groups (predominantly one or two) that can contribute to the COVID-19 transmission/mortality rate. The current study effort is designed to remedy this by analyzing COVID-19 transmission/mortality rates considering a comprehensive set of factors in a unified framework. Method We study two per capita dependent variables: (1) daily COVID-19 transmission rates and (2) total COVID-19 mortality rates. The first variable is modeled using a linear mixed model while the later dimension is analyzed using a linear regression approach. The model results are augmented with a sensitivity analysis to predict the impact of mobility restrictions at a county level. Findings Several county level factors including proportion of African-Americans, income inequality, health indicators associated with Asthma, Cancer, HIV and heart disease, percentage of stay at home individuals, testing infrastructure and Intensive Care Unit capacity impact transmission and/or mortality rates. From the policy analysis, we find that enforcing a stay at home order that can ensure a 50% stay at home rate can result in a potential reduction of about 30% in daily cases. Interpretation The model framework developed can be employed by government agencies to evaluate the influence of reduced mobility on transmission rates at a county level while accommodating for various county specific factors. Based on our policy analysis, the study findings support a county level stay at home order for regions currently experiencing a surge in transmission. The model framework can also be employed to identify vulnerable counties that need to be prioritized based on health indicators for current support and/or preferential vaccination plans (when available). Funding None. RESEARCH IN CONTEXT Evidence before this study We conducted an exhaustive review of studies examining the factors affecting COVID-19 transmission and mortality rates at an aggregate spatial location such as national, regional, state, county, city and zip code levels. The review considered articles published in peer-reviewed journals (via PubMed and Web of Science) and working articles uploaded in preprint platforms (such as medRxiv). A majority of these studies focused on a small number of counties (up to 100 counties) and considered COVID-19 data only up to the month of April. While these studies are informative, cases in the US grew substantially in recent months. Further, earlier studies have considered factors selectively from the four variable groups - socio-demographics, health indicators, mobility trends, and health care infrastructure attributes. The exclusion of variables from these groups is likely to yield incorrect/biased estimates for the factors considered. Added value of this study The proposed study enhances the coverage of COVID-19 data in our analysis. Spatially, we consider 1258 counties encompassing 87% of the total population and 96% of the total confirmed COVID-19 cases. Temporally, we consider data from March 25th to July 3rd, 2020. The model system developed comprehensively examines factors affecting COVID-19 from all four categories of variables described above. The county level daily transmission data has multiple observations for each county. To accommodate for these repeated measures, we employ a linear mixed modeling framework for model estimation. The model estimation results are augmented with policy scenarios imposing hypothetical mobility restrictions. Implications of all the available evidence The proposed framework and the results can allow policy makers to (a) evaluate the influence of population behavior factors such as mobility trends on virus transmission (while accounting for other county level factors), (b) identify priority locations for health infrastructure support as the pandemic evolves, and (c) prioritize vulnerable counties across the country for vaccination (when available).