Neighborhood-level inequalities and influencing factors of COVID-19 incidence in Berlin based on Bayesian spatial modelling

Numerous studies have explored influencing factors in COVID-19, yet empirical evidence on spatiotemporal dynamics of COVID-19 inequalities concerning both socioeconomic and environmental factors at an intra-urban scale is lacking. This study, therefore, focuses on neighborhood-level spatial inequalities of the COVID-19 incidences in relation to socioeconomic and environmental factors for Berlin-Neuko center dot lln, Germany, covering six pandemic periods (March 2020 to December 2021). Spatial Bayesian negative binomial mixed-effect models were employed to identify influencing factors and risk patterns for different periods. We identified that (1) influencing factors and relative risks varied across time and space, with sociodemographic factors exerting a stronger influence over environmental features; (2) as the most identified predictors, the population with migrant backgrounds was positively associated, and the population over 65 was negatively associated with COVID-19 incidence; (3) certain neighborhoods consistently faced elevated risks of COVID-19 incidence. This study highlights potential structural health inequalities within migrant communities, associated with lower socioeconomic status and a higher risk of COVID-19 incidence across diverse pandemic periods. Our findings indicate that locally tailored interventions for diverse citizens are essential to address health inequalities and foster a more sustainable urban environment.