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

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 (January 20, 2020 to July 1, 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; South Korea; pandemics; socioeconomic factors; spatial regression.

Figure 1

Conceptual model of the causal relationship between the SARS-CoV-2 and area health/SE determinants. Abbreviations: material capital (MC), human capital (HC), social capital (SC), socioeconomic status (SES). Subscripts (i, j, k) indicate the number of variables used from the data sources. * Material, Human and Social Capital refers to latent structural components of the SES and COVID-19-specific determinants. Health and SES connected by arrows indicate the inter-relatedness of area health and SES, hereinafter, denoted as a health/SE. ⸸ Area-health/SE themes identified relevant to COVID-19 based on the current person and population-level literature. As per Coleman’s social theory and contributing data underlying each health/SE theme, crowding, healthcare access and social distancing relates to material capital, health behaviour and area morbidity relates to human capital, whereas, crowding, education and population mobility to social capital. Modified from source [9,24]. Data sources: Korean Community Health Survey by the KCDC, Health Insurance Statistics by the National Health Insurance Services, Disability Status by the Ministry of Health and Welfare, Death Cause Statistics by the National Statistics Agency, Korean Census Bureau, Internal Migration Statistics by the Statistics Korea, and the State of Urban Planning Report by the Ministry of Land, Infrastructure, Transport, and Tourism.

Figure 2

The spatial distribution of COVID-19 cases across pandemic phases. Early phase: from 20 January to 20 March 2020. Middle phase: 21 March to 15 April 2020. Late phase: 16 April to 1 July 2020. The blue shades and bar heights both indicate the number of COVID-19 cases during each in each district.

Figure 3

Relative Risk of COVID-19 associated with area health and SES determinants (GNBR models). Each panel shows the relative risk and its 95% confidence intervals associated with each thematic area. Colours represent the pandemic’s early (20 January to 20 March 2020), middle (21 March to 15 April 2020), and late phases (16 April to 1 July 2020). The dashed line shows the reference level (1). Values over or below the reference line indicate statistically significant results at α = 0.05. Corresponding p-values can be found in Table S5.

Figure 4

Spatial variation in the relative risk of COVID-19 associated with area-health and SES themes in the early, middle, and late phases of the pandemic (GWNBR models). In the maps, the blue colour gradient corresponds with larger (darker) to lower (lighter) relative risk. Areas in white indicate the relative risks are statistically not significant (α = 0.05). The figure consists of (A–C) columns respectively referring to the pandemic’s early (20 January to 20 March 2020), middle (21 March to 15 April 2020), and late phases (16 April to 1 July 2020).