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Precision Mapping of COVID-19 Vulnerable Locales by Epidemiological and Socioeconomic Risk Factors, Developed Using
Bayarmagnai Weinstein1, Alan R da Silva2, Dimitrios E Kouzoukas3
1Department of Environmental Health Sciences, School of Public Health, University at Albany, Rensselaer, New York, NY 12144, USA.
International Journal of Environmental Research and Public Health
|January 15, 2021
Summary
Geographically weighted negative binomial regression mapped COVID-19 risk, showing higher risk with increased morbidity and mobility, and lower risk with better social distancing. Effective interventions reduced spatial risks over time.
Area of Science:
- Epidemiology
- Public Health
- Spatial Analysis
Background:
- COVID-19 disproportionately affected socioeconomically disadvantaged groups.
- Understanding spatial risk factors is crucial for pandemic control.
Purpose of the Study:
- To map COVID-19 risk using epidemiological and socioeconomic factors.
- To develop a replicable framework for pandemic risk assessment.
Main Methods:
- Geographically Weighted Negative Binomial Regression (GWNBR) applied to South Korean COVID-19 data (Jan-Jul 2020).
- Composite indexes created for socioeconomic and epidemiological themes via principal component analysis.
Main Results:
- COVID-19 risk correlated positively with area morbidity, risky health behaviors, crowding, and population mobility.
- Risk correlated negatively with social distancing, healthcare access, and education levels.
- Observed declining risks and spatial shifts indicated successful public health interventions.
Conclusions:
- GWNBR provides precision mapping for COVID-19 risk factors.
- The methodological framework is adaptable for future pandemic preparedness and response.
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