Center for Value-Based Care Research, Cleveland Clinic, Cleveland, Ohio, USA
Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran.
BMJ Health Care Inform. 2023 Jul;30(1). doi: 10.1136/bmjhci-2022-100703.
More 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.
We 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.
Our 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.
SVI 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.
截至 2022 年 8 月，美国已报告超过 9300 万例 COVID-19 病例和超过 100 万例 COVID-19 死亡病例。大流行的不成比例影响及其对弱势社区的严重影响引起了人们的关注。本研究旨在确定和排名社会脆弱性指数（SVI）因素，这些因素在大流行初期对美国南部 COVID-19 的传播具有高度预测性。
我们使用极端梯度增强（XGBoost）机器学习方法和 SVI 数据，以及美国南部所有县的 COVID-19 病例数，来预测一个县首例病例后 30 天内阳性病例的数量。
我们的结果表明，移动房屋的百分比是预测 COVID-19 增加的最重要特征。此外，每平方英里的人口密度、人均收入、每十户以上住房的比例、贫困人口的比例以及没有高中文凭的人的比例分别是 COVID-19 社区传播的重要预测因素。
SVI 可用于评估社区对 COVID-19 传播的脆弱性或弹性，并可帮助识别 COVID-19 传播风险较高的社区。
