Social vulnerability and initial COVID-19 community spread in the US South: a machine learning approach

Author:

Tatar MoosaORCID,Faraji Mohammad Reza,Wilson Fernando A

Abstract

Background and objectivesMore than 93 million COVID-19 cases and more than 1 million COVID-19 deaths have been reported in the USA by August 2022. The disproportionate effect of the pandemic and its severe impact on vulnerable communities raised concerns. This research aimed to identify and rank Social Vulnerability Index (SVI) factors highly predictive of the spread of COVID-19 in the US South at the beginning of the pandemic.MethodsWe used Extreme Gradient Boosting (XGBoost) machine learning methodology and SVI data, and the number of COVID-19 cases across all counties in the US South to predict the number of positive cases within 30 days of a county’s first case.ResultsOur results showed that the percentage of mobile homes is the most important feature in predicting the increase in COVID-19. Also, population density per square mile, per capita income, percentage of housing in structures with 10+ units, percentage of people below poverty and percentage of people with no high school diploma are important predictors of COVID-19 community spread, respectively.ConclusionsSVI can help assess the vulnerability or resilience of communities to the spread of COVID-19 and can help identify communities at high risk of COVID-19 spread.

Publisher

BMJ

Reference33 articles.

1. Center for Systems Science and Engineering (CSSE) . Global cases by the center for systems science and engineering (CSSE) at Johns Hopkins University (JHU)" Johns Hopkins CSSE. Available: https://github.com/CSSEGISandData/COVID-19 [Accessed 24 Aug 2022].

2. Social vulnerability and racial inequality in COVID-19 deaths in Chicago;Kim;Health Educ Behav,2020

3. County-Level Association of Social Vulnerability with COVID-19 Cases and Deaths in the USA

4. Impact of social determinants of health on the emerging COVID-19 pandemic in the United States;Singu;Front Public Health,2020

5. Baum CF Henry M . Socioeconomic factors influencing the spatial spread of COVID-19 in the United States. SSRN Journal 2020. doi:10.2139/ssrn.3614877

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3