Measuring the impact of climate and air quality on COVID-19 transmission dynamics: a tale of two cities in pandemic preparedness

This comparative study investigates the non-linear influence of nine meteorological and air quality factors on COVID-19 transmission in Daegu and Seoul, South Korea, from January to July 2020. Employing Generalized Additive Models (GAMs), percentile-based risk zone identification, and K-means clustering, our analysis demonstrated that environmental factors are highly region-specific, challenging universal public health approaches. In Daegu, the risk profile was strongly shaped by climatic factors (GAM Explained Deviance: 63.0%), exhibiting a U-shaped temperature effect with elevated risk at both extremes. A primary high-risk zone appeared in cool, moderately dry conditions, averaging 278.8 cases per day, consistent with the Cold environmental regime. Conversely, Seoul’s risk profile indicated significant air pollutant influence (GAM Explained Deviance: 48.3%), showing monotonic positive relationships for temperature, NO \(_{2}\) , and O \(_{3}\) . Crucially, the data revealed a strong positive correlation between temperature and O \(_{3}\) (r = +0.794), indicating warmer periods feature elevated O₃, increasing respiratory sensitivity. A primary high-risk zone in Seoul occurred in warm, humid conditions (averaging 29.5 cases per day), reinforcing higher counts in Warm environmental regimes directly opposite to Daegu. These findings underscore the need for tailored, data-driven environmental early warning systems (EEWS) specific to regional characteristics, enhancing preparedness for future pandemic threats.