GIS-based vulnerability analysis of the United States to COVID-19 occurrence

Abstract

The outbreak of COVID-19 in the United States has resulted in over 11.2 million cases and over 240 thousand deaths. COVID-19 has affected the society in unprecedented way with its socioeconomic impact yet to be determined. This study aimed at assessing the vulnerability of the US at the county-level to COVID-19 using the pandemic data from January to June of the year 2020. The study considered the following critical factors: population density, elderly population, racial/ethnic minority population, diabetics, income, and smoking adults. Pearson’s correlation analysis was performed to validate the independence of the factors. Spatial correlations between the COVID-19 occurrence and the factors were examined using Jaccard similarity analysis, which revealed relatively high correlation. A vulnerability to COVID-19 map with a five-level Likert scale was created using Logistic Regression Analysis in ArcGIS. The map showed close agreement in seven representative states, which were selected based on COVID-19 cases including NY, CA, FL, TX, OH, NC, and MT with R2 values between 0.684 and 0.731 with Root Mean Squared Error (RMSE) values between ±0.033 and ±0.057. Furthermore, vulnerability levels from ‘High’ to ‘Very High’ were obtained for the top ten counties with the highest COVID-19 cases with residual values less than or equal to 0.04. The method and resulted vulnerability map can aid in COVID-19 response planning, prevention programs and devising strategies for controlling COVID-19 and similar pandemics in the future.