Space-time exploration of COVID-19 data and local risk factors in Berlin: the example of the district of Neukölln

• Applicants: Professorin Dr. Tobia Lakes; Privatdozent Dr. Nicolai Savaskan
• Subject Area: Human Geography ; Public Health, Healthcare Research, Social and Occupational Medicine
• Term: from 2021 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 492361591

In March, 2020, the WHO declared the outbreak of the coronavirus disease a global pandemic. With the onset of the COVID-19 pandemic, among others, three characteristics become distinct: 1. The close connection between health and a number of other factors. Not only biomedical factors are drivers of the pandemic, but also environmental, social and economic parameters play their part in the spread of the coronavirus. This has been shown for the individual level in other countries but not yet in Germany. 2. There is a distinct spatial pattern and underlying process in the number of COVID-19 cases. These spatial differences are not only observable on a national but also on a regional and local level. Health monitoring in Germany, however, generally uses aggregated regional data in an attempt to determine the driving forces for health-related problems in an ecological analysis. An initial analysis was done by the Berlin Senate Administration for Health, Nursing and Equal Opportunity at the district level to show the statistical significance of some of the determinants of the COVID-pandemic in late summer 2020. As the Berlin districts are very heterogenous in regard to their population, their social structure and the built environment, we believe that the district level is not differentiated enough for a robust analysis. This project will take advantage of the unique situation Berlin provides with the system of small-scale lifeworld-oriented areas (LOR, similar to neighbourhoods) that are used by most units of the city and district administrations to aggregate their data. This pandemic hence revealed that there is substantial potential to improve the workflows and techniques to assess, analyse, monitor and adequately address this pandemic (and potential other health) situations with policy measures. This project aims to develop and apply a set of innovative spatiotemporal data analysis techniques to assess, analyse and monitor the COVID-19 pandemic on a detailed spatial level of LOR neighbourhoods. We use administrative data from the data-pool in connection with data of the health department in regard to the spread of the coronavirus pandemic at the LOR neighbourhood level in in Berlin-Neukölln. We aim to investigate the spatio-temporal distribution of COVID-19. We argue that there is a meaningful connection between socio-economic factors and the distribution of COVID-19 cases. Research that specifically addresses COVID-19 cases on small geographical units and their connection with socio-economic variables of the neighborhood are missing. This study seeks to analyse socio-economic data with COVID-19 cases on small administrative units. The findings help to develop a Risk Index in Berlin and - on a very detailed spatial level of neighbourhoods - in Berlin-Neukölln. By using verified COVID-19 cases with a high spatial resolution collected by the health office in Neukölln, the identified neighbourhoods will be checked for clusters of COVID-19 outbreaks.

• DFG Programme: Research Grants
• International Connection: Switzerland
• Cooperation Partner: Dr. Oliver Grübner