A machine learning-driven spatio-temporal vulnerability appraisal based on socio-economic data for COVID-19 impact prevention in the U.S. counties

Mohammad Moosazadeh, Pouya Ifaei, Amir Saman Tayerani Charmchi, Somayeh Asadi, Chang Kyoo Yoo

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

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.

Original languageEnglish (US)
Article number103990
JournalSustainable Cities and Society
Volume83
DOIs
StatePublished - Aug 2022

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Civil and Structural Engineering
  • Renewable Energy, Sustainability and the Environment
  • Transportation

Fingerprint

Dive into the research topics of 'A machine learning-driven spatio-temporal vulnerability appraisal based on socio-economic data for COVID-19 impact prevention in the U.S. counties'. Together they form a unique fingerprint.

Cite this