A methodological blueprint to identify COVID-19 vulnerable locales by socioeconomic factors, developed using South Korean data

COVID-19 has more severely impacted socioeconomically (SES) disadvantaged populations. Lack of SES measurements and inaccurately identifying high-risk locales can hamper COVID-19 mitigation efforts. Using South Korean COVID-19 incidence data (January 20 through July 1, 2020) and established social theoretical approaches, we identified COVID-19-specific SES factors. Principal component analysis created composite indexes for each SES factor, while Geographically Weighted Negative Binomial Regressions mapped a continuous surface of COVID-19 risk for South Korea. High area morbidity, risky health behaviors, crowding, and population mobility elevated area risk for COVID-19, while improved social distancing, healthcare access, and education decreased it. Our results indicated that falling SES-related COVID-19 risks and spatial shift patterns over three consecutive time periods reflected the implementation of reportedly effective public health interventions. While validating earlier studies, this study introduced a methodological blueprint for precision targeting of high-risk locales that is globally applicable for COVID-19 and future pandemics.