ArticleSustainable cities and society2022
A machine learning-driven spatio-temporal vulnerability appraisal based on socio-economic data for COVID-19 impact prevention in the U.S. counties.
Article in Sustainable cities and society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- A multi-phase, multi-method assessment of national COVID-19 vaccination performance with equity analysis.Scientific reports · 2026Article
- Machine learning and public health policy evaluation: research dynamics and prospects for challenges.Frontiers in public health · 2025Review
- Inequity in Washington state covid-19-related crowdfunding.Social sciences & humanities open · 2024Article
- Construction and validation of risk prediction models for pulmonary embolism in hospitalized patients based on different machine learning methods.Frontiers in cardiovascular medicine · 2024Article
- A cross-jurisdictional comparison on residential waste collection rates during earlier waves of COVID-19.Sustainable cities and society · 2023Article
- Risk perception of compound emergencies: A household survey on flood evacuation and sheltering behavior during the COVID-19 pandemic.Sustainable cities and society · 2023Article
- Investigating the impacts of socio-economic vulnerability indicators on COVID-19: A case study of Yazd, Iran.International journal of disaster risk reduction : IJDRR · 2023Article
- Advances in Microbial Diagnostics: Machine Learning and Nanotechnology for Zoonotic Disease Control.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnologyReview
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A mature and hybrid machine-learning model is verified by mature empirical analysis to measure county-level COVID-19 vulnerability and track the impact of the imposition of pandemic control policies in the U.S. A total of 30 county-level social, economic, and medical variables and a timeline of the imposed policies constitutes a COVID-19 database. A hybrid feature-selection model composed of four machine-learning algorithms is developed to emphasize the regional impact of community features on the case fatality rate (CFR). A COVID-19 vulnerability index (COVULin) is proposed to measure the county's vulnerability, the effects of model's parameters on mortality, and the efficiency of control policies. The results showed that the dense counties in which minority groups represent more than 45% of the population and those with poverty rates greater than 24% were the most vulnerable counties during the first and the last pandemic peaks, respectively. Highly-correlated CFR and COVULin scores indicated a close agreement between the model outcomes and COVID-19 impacts. Counties with higher poverty and uninsured rates were the most resistant to government intervention. It is anticipated that the proposed model can play an essential role in identifying vulnerable communities and help reduce damages during long-term alike disasters.
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Registered trials
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