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

Health and Fitness

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

X. Ren, Z. Mi, et al.

This groundbreaking study explores how socioexposomic factors relate to COVID-19 outcomes in New Jersey, revealing strong connections between mortality rates and factors like NO2 exposure and population density. Conducted by Xiang Ren, Zhongyuan Mi, and Panos G. Georgopoulos, this research uncovers insights that could reshape public health strategies.

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Playback language: English
Abstract
This study investigates the association between socioexposomic factors and COVID-19 outcomes in New Jersey using geostatistical and machine learning models. The researchers found robust positive associations between COVID-19 mortality and factors like NO2 exposure, population density, minority percentage, and low educational attainment. While excluding long-term care facility deaths had minimal impact on most correlations, model structure significantly influenced findings. Machine learning models revealed consistent nonlinear associations not captured by geostatistical models.
Publisher
Journal of Exposure Science & Environmental Epidemiology
Published On
Feb 01, 2023
Authors
Xiang Ren, Zhongyuan Mi, Panos G. Georgopoulos
Tags
COVID-19
socioexposomic factors
mortality
NO2 exposure
population density
minority percentage
machine learning

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