Epidemiological insights and geographic clusters for COVID-19 in Taiwan using a mixture scan statistic

Abstract

The COVID-19 pandemic has posed unprecedented public health challenges worldwide, necessitating a comprehensive understanding of its transmission dynamics. This study examines the correlation between COVID-19 transmission and various risk factors, focusing on the impact of population structure and socio-economic conditions in Taiwan. By analyzing official government databases, we explore how factors such as population density, dependency ratios, and socio-economic environment influence the spread of COVID-19. Our findings highlight that densely populated areas, along with regions characterized by higher child dependency ratios and a significant number of low- and middle-income households, exhibit higher transmission rates. This research underscores the importance of considering socio-economic disparities and healthcare access in developing effective public health strategies. Furthermore, we utilize a mixture scan statistic to identify disease hotspots, taking into account spatial correlation and covariate effects. This approach can detect clusters based on known risk factors and help to assess possible unknown geographic risks, facilitating targeted interventions and resource allocation. Our study contributes to the broader understanding of COVID-19 transmission dynamics, offering insights into the importance of integrating socio-economic factors and spatial analysis in pandemic response efforts.