Objective To perform a geospatial and temporal trend analysis for coronavirus disease-2019 (COVID-19) in a Midwest community to identify and characterize hotspots for COVID-19. Methods We conducted a population-based longitudinal surveillance assessing the semi-monthly geospatial trends of the prevalence of test confirmed COVID-19 cases in Olmsted County, Minnesota, from March 11, 2020, to October 31, 2020. As urban areas accounted for 84% of population and 86% of all COVID-19 cases in Olmsted County, MN, we determined hotspots for COVID-19 in urban areas of Olmsted County (Rochester and other small cities), MN during the study period by using kernel density analysis with a half-mile bandwidth. Results As of October 31, 2020, a total of 37,141 subjects (30%) were tested at least once of whom 2,433 (6.6%) tested positive. Testing rates among race groups were similar: 29% (African American), 30% (Hispanic), 25% (Asian), and 31% (White). Ten urban hotspots accounted for 590 cases at 220 addresses (2.68 case/address), compared to 1,843 cases at 1,292 addresses in areas outside hotspots (1.43 case/address). Overall, 12% of population residing in hotspot areas accounted for 24% of all COVID-19 cases. Hotspots were concentrated in neighborhoods with low-income apartments and mobile home communities. People living in hotspots tended to be minorities and from lower socioeconomic background. Conclusion Geographic and residential risk factors might significantly account for overall burden of COVID-19 and its associated racial/ethnic and socioeconomic disparities. Results could geospatially guide community outreach efforts (e.g., testing/tracing, and vaccine roll out) for populations at risk for COVID-19.
Role of Geographic Risk Factors in COVID-19 Epidemiology: Longitudinal Geospatial Analysis