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.
【저자키워드】 COVID-19, Pandemics, South Korea, Socioeconomic Factors, Spatial regression, 【초록키워드】 pandemic, social distancing, risk, risk factor, principal component analysis, Health, healthcare, morbidity, Public health interventions, epidemiological, incidence, Korean, Support, Precision, South, populations, risk of COVID-19, effective, provide, reflected, mapped, impacted, methodological, 【제목키워드】 Korean, South, mapping, Developed,