Different waves, same places? Recurrent areas of high relative risk across the six waves of COVID-19 in the city of Málaga. A spatial analysis in the intraurban scale

Abstract

The COVID-19 pandemic fostered the generation and availability of georeferenced health data, which made it possible to conduct high-resolution spatial analyses of health patterns. However, most studies have relied on aggregated spatial units and limited time periods, restricting insights into localized disease dynamics. This study identifies persistent COVID-19 hotspots across six waves using household-level georeferenced case data and examines socioeconomic and urban factors contributing to elevated risk. We analyzed 60,165 georeferenced COVID-19 cases in Málaga, Spain (2020–2022), adjusting for population density. Relative risk surfaces were generated for each wave, and a Boolean overlay analysis identified statistically significant (p < 0,05) high-risk zones consistently present across all six waves of the acute phase. These zones were compared with census-based socioeconomic and urban variables at a 250×250 meter resolution. Results show that high-risk areas remained geographically stable across waves, covering up tp 21 km² considering all periods (55% of the city), mainly in northern and southeastern peripheries. Areas exhibiting high-risk recurrence in 4-6 waves align with lower-income neighborhoods, higher unemployment rates, mixed residential types, and smaller housing sizes. Since our analysis used census-level population data for these variables, confidence intervals were not applicable. However, a non-parametric Kruskal-Wallis test was performed to assess if there were any statistically significant differences between these variables across zones by the number of times they exhibited higher relative risk. The persistence of high-risk areas suggests that there are structural factors that shape the spatial distribution of infectious disease transmission, despite differences in mitigation and preventions measures during the acute phase of the pandemic. These findings underscore the need for fine-scale spatial analysis in urban epidemiology to guide targeted public health interventions, despite challenges related to heterogeneous spatial data.