Authors: Ming-Hsiang Tsou*, San Diego State University
Topics: Geographic Information Science and Systems, Cartography, Medical and Health Geography
Keywords: Health Disparity, COVID-19, Neighborhoods, Socio-economic status
Session Type: Virtual Paper
Start / End Time: 8:00 AM / 9:15 AM
Room: Virtual 8
Presentation File: No File Uploaded
Neighborhood characteristics, including the composition of race/ethnicity and elderly, socio-economic status (SES) and geographic location, contribute to disparities in who contracts COVID-19 and their treatment outcomes (e.g., hospitalization and/or death). An effective detection and analysis of these factors and their potential impacts can help improve decision-making and health resource management. This study compared these neighborhood and geographic factors from American Census Survey (ACS) with the COVID-19 confirmed cases at the Zip Code level in San Diego. We are conducting health disparity analysis by using geographic information systems (GIS) and spatiotemporal statistical approaches. The preliminary correlation analysis identifies several socioeconomic factors that show interesting patterns with COVID-19 confirmed cases. Africa American population are highly correlated with the COVID-19 cases at the Zip Code Level. The Spanish-Speaking group also shows a high correlation. The non-hispanic white population shows a high negative correlation. As for the age groups, age groups of 15 to 24 and 25 to 44 are positively correlated with COVID-19 cases, which mean teenagers are more likely to get involved with COVID-19 situation. Even though the old generation (Older than 65) are suffering more in COVID-19, they tend to be more careful toward the COVID-19 and show sigificant negative correlation to the COVID-19 confirmed cases.