2020/09/22 by Anuj Tiwari, Tiwari, Anuj, Arya V. Dadhania +5
Economics, Econometrics and Finance · Mathematics · Psychology · #Applications (stat.AP) #COVID-19 Pandemic Impacts #COVID-19 and Mental Health #COVID-19 epidemiological studies #FOS: Computer and information sciences #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2009.10808
openalex publication_date 2020/09/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
COVID19 is now one of the most leading causes of death in the United States.\nSystemic health, social and economic disparities have put the minorities and\neconomically poor communities at a higher risk than others. There is an\nimmediate requirement to develop a reliable measure of county-level\nvulnerabilities that can capture the heterogeneity of both vulnerable\ncommunities and the COVID19 pandemic. This study reports a COVID19\nVulnerability Index (C19VI) for identification and mapping of vulnerable\ncounties in the United States. We proposed a Random Forest machine learning\nbased COVID19 vulnerability model using CDC sociodemographic and\nCOVID19-specific themes. An innovative COVID19 Impact Assessment algorithm was\nalso developed using homogeneity and trend assessment technique for evaluating\nseverity of the pandemic in all counties and train RF model. Developed C19VI\nwas statistically validated and compared with the CDC COVID19 Community\nVulnerability Index (CCVI). Finally, using C19VI along with census data, we\nexplored racial inequalities and economic disparities in COVID19 health\noutcomes amongst different regions in the United States. Our C19VI index\nindicates that 18.30% of the counties falls into very high vulnerability class,\n24.34% in high, 23.32% in moderate, 22.34% in low, and 11.68% in very low.\nFurthermore, C19VI reveals that 75.57% of racial minorities and 82.84% of\neconomically poor communities are very high or high COVID19 vulnerable regions.\nThe proposed approach of vulnerability modeling takes advantage of both the\nwell-established field of statistical analysis and the fast-evolving domain of\nmachine learning. C19VI provides an accurate and more reliable way to measure\ncounty level vulnerability in the United States. This index aims at helping\nemergency planners to develop more effective mitigation strategies especially\nfor the disproportionately impacted communities.\n