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.

摘要

在美国，COVID-19的暴发导致超过1,120万例病例和24万多人死亡。COVID-19以前所未有的方式影响了社会，其社会经济影响尚待确定。这项研究旨在使用2020年1月至6月的大流行数据评估县级美国对COVID-19的脆弱性。该研究考虑了以下关键因素：人口密度，老年人口，种族/少数民族人口，糖尿病患者，收入和吸烟的成年人。进行了Pearson的相关分析以验证因素的独立性。使用Jaccard相似性分析检查了COVID-19发生与这些因素之间的空间相关性，发现相关性相对较高。使用ArcGIS中的Logistic回归分析创建了具有五级李克特比例的COVID-19映射漏洞。该图显示了七个代表性州的紧密一致性，这七个州是根据COVID-19案例选择的，包括NY，CA，FL，TX，OH，NC和MT与R^2个值在0.684和0.731之间，均方根误差（RMSE）值在±0.033和±0.057之间。此外，对于残差值小于或等于0.04的COVID-19病例数最高的前十个县，从“高”到“非常高”获得了脆弱性级别。该方法和所产生的漏洞图可以帮助制定COVID-19响应计划，制定预防计划并制定策略，以在将来控制COVID-19和类似的流行病。