Exploration of the COVID-19 pandemic at the neighborhood level in an intra-urban setting

Abstract

The COVID-19 pandemic represents a worldwide threat to health. Since its onset in 2019, the pandemic has proceeded in different phases, which have been shaped by a complex set of influencing factors, including public health and social measures, the emergence of new virus variants, and seasonality. Understanding the development of COVID-19 incidence and its spatiotemporal patterns at a neighborhood level is crucial for local health authorities to identify high-risk areas and develop tailored mitigation strategies. However, analyses at the neighborhood level are scarce and mostly limited to specific phases of the pandemic. The aim of this study was to explore the development of COVID-19 incidence and spatiotemporal patterns of incidence at a neighborhood scale in an intra-urban setting over several pandemic phases (March 2020-December 2021). We used reported COVID-19 case data from the health department of the district Berlin-Neukölln, Germany, additional socio-demographic data, and text documents and materials on implemented public health and social measures. We examined incidence over time in the context of the measures and other influencing factors, with a particular focus on age groups. We used incidence maps and spatial scan statistics to reveal changing spatiotemporal patterns. Our results show that several factors may have influenced the development of COVID-19 incidence. In particular, the far-reaching measures for contact reduction showed a substantial impact on incidence in Neukölln. We observed several age group-specific effects: school closures had an effect on incidence in the younger population (< 18 years), whereas the start of the vaccination campaign had an impact primarily on incidence among the elderly (> 65 years). The spatial analysis revealed that high-risk areas were heterogeneously distributed across the district. The location of high-risk areas also changed across the pandemic phases. In this study, existing intra-urban studies were supplemented by our investigation of the course of the pandemic and the underlying processes at a small scale over a long period of time. Our findings provide new insights for public health authorities, community planners, and policymakers about the spatiotemporal development of the COVID-19 pandemic at the neighborhood level. These insights are crucial for guiding decision-makers in implementing mitigation strategies.

Keywords: COVID-19; age groups; public health measure; spatial scan statistics; spatiotemporal analysis; urban health.

Figure 1

Built-up and demographic characteristics of Neukölln: (A) built-up structures at block level*, and (B) population density at block level*, (C) socioeconomic status index** (calculated on the basis of unemployment rate, child poverty and people receiving social benefits), and (D) share of people >65 years old at planning unit level; (E) location of Neukölln in Berlin. Data source: Senate for Building, Housing and Transport, Berlin (44) and Statistics Office of Berlin-Brandenburg (43). *The block level is the smallest subdivision of the Berlin urban area in the regional reference system (43). **There is also the class “high” in Berlin. But no planning units are classified as “high” in Berlin-Neukölln.

Figure 2

Development of incidence across pandemic phases, including the four waves and two summer plateaus (SP) during the study period. Top: incidence in Neukölln over time with far-reaching public health and social measures (bold), distinct measures/influencing factors (regular), and distinct measures/influencing factors in Neukölln (italic); middle: age group-specific incidence; bottom: school holidays and variant of concern (VOC). Detailed descriptions of each measure can be found in Supplementary Table 2.

Figure 3

Cumulative incidence rate maps across the six pandemic phases, including the four waves (A,C,D,F) and two summer plateaus (SP) (B,E) during the study period.

Figure 4

Spatial clusters of high-risk (red) and low-risk (blue) clusters of COVID-19 incidence across the six pandemic phases, including the four waves (A,C,D,F) and two summer plateaus (SP) (B,E) during the study period. The buffers represent the circular windows that were detected as significant spatial clusters. The size was determined by the centroids of the planning units located within the cluster.