ArticleBMJ health & care informatics2023
Social vulnerability and initial COVID-19 community spread in the US South: a machine learning approach.
Article in BMJ health & care informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Spatial variation in socio-economic vulnerability to Influenza-like Infection for the US population.PLoS computational biology · 2026Article
- Persistent poverty, glycemic control and adverse COVID-19 outcomes: a retrospective study using real-world data.BMC public health · 2025Article
- Unveiling Community Vulnerability to COVID-19 Incidence: A Population-Based Spatial Analysis in Clark County, Nevada.International journal of environmental research and public health · 2025Article
- Spatiotemporal patterns of youth isolation and loneliness in the US: a geospatial analysis of Crisis Text Line data (2016-2022).GeoJournal · 2024Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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