Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches.

Journal: Journal of exposure science & environmental epidemiology

Published Date: Feb 1, 2023

Abstract

BACKGROUND: Disparities in adverse COVID-19 health outcomes have been associated with multiple social and environmental stressors. However, research is needed to evaluate the consistency and efficiency of methods for studying these associations at local scales.

Authors

Xiang Ren

The Bradley Department of Electrical and Computer Engineering , Virginia Tech , Blacksburg , Virginia 24061 , United States.
Zhongyuan Mi

Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ, 08854, USA.
Panos G Georgopoulos

Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ, 08854, USA. panosg@ccl.rutgers.edu.

Keywords

Air Pollution COVID-19 Humans Machine Learning Models, Statistical New Jersey Pandemics SARS-CoV-2 Socioeconomic Factors

External Resources

View on PubMed Access via DOI PubMed (36725924)

Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches.

Abstract

Authors

Keywords

External Resources

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