Analyzing Socioeconomic Factors and Health Disparity of COVID-19 Spatiotemporal Spread Patterns at Neighborhood Levels in San Diego County

This study analyzed spatiotemporal spread patterns of COVID-19 confirmed cases at the zip code level in the County of San Diego and compared them to neighborhood social and economic factors. We used correlation analysis, regression models, and geographic weighted regression to identify important factors and spatial patterns. We broke down the temporal confirmed case patterns into four stages from 1 April 2020 to 31 December 2020. The COVID-19 outbreak hotspots in San Diego County are South Bay, El Cajon, Escondido, and rural areas. The spatial patterns among different stages may represent fundamental health disparity issues in neighborhoods. We also identified important variables with strong positive or negative correlations in these categories: ethnic groups, languages, economics, and education. The highest association variables were Pop5andOlderSpanish (Spanish-speaking) in Stage 4 (0.79) and Pop25OlderLess9grade (Less than 9^thgrade education) in Stage 4 (0.79). We also observed a clear pattern that regions with more well-educated people have negative associations with COVID-19. Additionally, our OLS regression models suggested that more affluent populations have a negative relationship with COVID-19 cases. Therefore, the COVID-19 outbreak is not only a medical disease but a social inequality and health disparity problem.