Precision Mapping of COVID-19 Vulnerable Locales by Epidemiological and Socioeconomic Risk Factors, Developed Using South Korean Data

Bayarmagnai Weinstein, Alan R. da Silva, Dimitrios E. Kouzoukas, Tanima Bose, Gwang Jin Kim, Paola A. Correa, Santhi Pondugula, YoonJung Lee, Jihoo Kim and David O. Carpenter
Additional contact information
Bayarmagnai Weinstein: Department of Environmental Health Sciences, School of Public Health, University at Albany, Rensselaer, New York, NY 12144, USA
Alan R. da Silva: Department of Statistics, University of Brasília, Brasília 70910-900, Brazil
Dimitrios E. Kouzoukas: Research Service, Edward Hines Jr. VA Hospital, Hines, IL 60141, USA
Tanima Bose: Institute for Clinical Neuroimmunology, Ludwig-Maximilian University of Munich, Planegg-Martinsried, 82152 Munich, Germany
Gwang Jin Kim: Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany
Paola A. Correa: Howard Hughes Medical Institute, Ashburn, VA 20147, USA
Santhi Pondugula: Department of Pharmacology & Therapeutics, University of Florida, Gainesville, FL 32610, USA
YoonJung Lee: Department of Pharmaceutical Sciences, School of Pharmacy, Texas Tech University Health Sciences Center, Amarillo, TX 79106, USA
Jihoo Kim: Department of Computer Science, Hanyang University, Seongdong-gu, Seoul 04763, Korea
David O. Carpenter: Department of Environmental Health Sciences, School of Public Health, University at Albany, Rensselaer, New York, NY 12144, USA

IJERPH, 2021, vol. 18, issue 2, 1-14

Abstract: COVID-19 has severely impacted socioeconomically disadvantaged populations. To support pandemic control strategies, geographically weighted negative binomial regression (GWNBR) mapped COVID-19 risk related to epidemiological and socioeconomic risk factors using South Korean incidence data (20 January 2020 to 1 July 2020). We constructed COVID-19-specific socioeconomic and epidemiological themes using established social theoretical frameworks and created composite indexes through principal component analysis. The risk of COVID-19 increased with higher area morbidity, risky health behaviours, crowding, and population mobility, and with lower social distancing, healthcare access, and education. Falling COVID-19 risks and spatial shifts over three consecutive time periods reflected effective public health interventions. This study provides a globally replicable methodological framework and precision mapping for COVID-19 and future pandemics.

Keywords: COVID-19; pandemics; socioeconomic factors; spatial regression; South Korea (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/2/604/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/2/604/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:2:p:604-:d:479076

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().