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Gis-based vulnerability analysis of the United States to COVID-19 occurrence. (Special Issue: COVID-19 one year on.)
Journal of Risk Research ; 24(3/4):416-431, 2021.
Article in English | GIM | ID: covidwho-1747026
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.
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Full text: Available Collection: Databases of international organizations Database: GIM Language: English Journal: Journal of Risk Research Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: GIM Language: English Journal: Journal of Risk Research Year: 2021 Document Type: Article