ArticleThe Science of the total environment2021
Using machine learning to develop a novel COVID-19 Vulnerability Index (C19VI).
Article in The Science of the total environment, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Can past variants of SARS-CoV-2 predict the impact of future variants? Machine learning for early warning of US counties at risk.Health care management science · 2025Article
- Applying Machine Learning Techniques to Implementation Science.Online journal of public health informatics · 2024Article
- An early warning precision public health approach for assessing COVID-19 vulnerability in the UK: the Moore-Hill Vulnerability Index (MHVI).BMC public health · 2023Article
- Preparing for the next outbreak: A review of indices measuring outbreak preparedness, vulnerability, and resilience.Preventive medicine reports · 2023Review
- Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties.Nature communications · 2023Article
- Multidimensional Machine Learning Model to Calculate a COVID-19 Vulnerability Index.Journal of personalized medicine · 2023Article
- Development of a COVID-19 Vulnerability Index (CVI) for the Counties and Residents of New Jersey, USA.International journal of environmental research and public health · 2023Article
- Incorporating resilience when assessing pandemic risk in the Arctic: a case study of Alaska.BMJ global health · 2023Review
- An overview of urban analytical approaches to combating the Covid-19 pandemic.Environment and planning. B, urban analytics and city science · 2023Article
- Spatial relationship between COVID-19 and previous infectious disease outbreaks: Evidence from South Korea.Heliyon · 2023Article
- Data driven contagion risk management in low-income countries using machine learning applications with COVID-19 in South Asia.Scientific reports · 2023Article
- Development of a composite regional vulnerability index and its relationship with the impacts of the COVID-19 pandemic.Computational urban science · 2023Article
- Community vulnerability to the COVID-19 pandemic: A narrative synthesis from an ecological perspective.Journal of global health · 2022Article
- A district-level vulnerability assessment of next COVID-19 variant (Omicron BA.2) in Uttarakhand using quantitative SWOT analysis.Environment, development and sustainability · 2022Article
- Exploring spatial distribution of social vulnerability and its relationship with the Coronavirus disease 2019: the Capital region of South Korea.BMC public health · 2022Article
- Assessment of community vulnerability during the COVID-19 pandemic: Hong Kong as a case study.International journal of applied earth observation and geoinformation : ITC journal · 2022Article
- A machine learning-driven spatio-temporal vulnerability appraisal based on socio-economic data for COVID-19 impact prevention in the U.S. counties.Sustainable cities and society · 2022Article
- The Impact of US County-Level Factors on COVID-19 Morbidity and Mortality.Journal of urban health : bulletin of the New York Academy of Medicine · 2022Article
- Suburban Road Networks to Explore COVID-19 Vulnerability and Severity.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
COVID-19 is now one of the most leading causes of death in the United States (US). Systemic health, social and economic disparities have put the minorities and economically poor communities at a higher risk than others. There is an immediate requirement to develop a reliable measure of county-level vulnerabilities that can capture the heterogeneity of vulnerable communities. This study reports a COVID-19 Vulnerability Index (C19VI) for identifying and mapping vulnerable counties. We proposed a Random Forest machine learning-based vulnerability model using CDC's sociodemographic and COVID-19-specific themes. An innovative 'COVID-19 Impact Assessment' algorithm was also developed for evaluating severity of the pandemic and to train the vulnerability model. Developed C19VI was statistically validated and compared with the CDC COVID-19 Community Vulnerability Index (CCVI). Finally, using C19VI and the census data, we explored racial inequalities and economic disparities in COVID-19 health outcomes. Our index indicates that 575 counties (45 million people) fall into the 'very high' vulnerability class, 765 counties (66 million people) in the 'high' vulnerability class, and 1435 counties (204 million people) in the 'moderate' or 'low' vulnerability class. Only 367 counties (20 million people) were found as 'very low' vulnerable areas. Furthermore, C19VI reveals that 524 counties with a racial minority population higher than 13% and 420 counties with poverty higher than 20% are in the 'very high' or 'high' vulnerability classes. The C19VI aims at helping public health officials and disaster management agencies to develop effective mitigation strategies especially for the disproportionately impacted communities.
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