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

2020年3月，世界卫生组织宣布新冠肺炎疫情为全球大流行。随着新冠肺炎疫情的爆发，有三个特点显现出来：1。健康与许多其他因素之间的密切联系。不仅生物医学因素是大流行的驱动因素，环境、社会和经济因素也在冠状病毒的传播中发挥了作用。这在其他国家的个人层面上已经得到了证明，但在德国还没有。COVID-19病例数存在明显的空间格局和潜在过程。这些空间差异不仅体现在国家层面，也体现在区域和地方层面。然而，德国的健康监测通常使用汇总的区域数据，试图在生态分析中确定与健康有关的问题的驱动因素。柏林参议院卫生、护理和平等机会管理局在地区一级进行了初步分析，以显示2020年夏末covid - 19大流行的一些决定因素的统计显著性。由于柏林各地区在人口、社会结构和建筑环境方面都非常异质，我们认为地区层面的差异不足以进行强有力的分析。该项目将利用柏林提供的小规模面向生活世界的区域（LOR，类似于社区）系统的独特情况，城市和地区管理部门的大多数单位都使用该系统来汇总数据。因此，这次大流行表明，有很大的潜力可以改进工作流程和技术，以采取政策措施来评估、分析、监测和充分处理这次大流行（以及潜在的其他卫生状况）。该项目旨在开发和应用一套创新的时空数据分析技术，在LOR社区的详细空间层面上评估、分析和监测COVID-19大流行。我们将数据池中的管理数据与卫生部门在Berlin-Neukölln中关于LOR社区一级冠状病毒大流行传播的数据相关联。我们的目的是调查COVID-19的时空分布。我们认为，社会经济因素与COVID-19病例分布之间存在有意义的联系。目前缺乏专门针对小地理单位的COVID-19病例及其与社区社会经济变量之间关系的研究。本研究旨在分析小行政单位COVID-19病例的社会经济数据。这些发现有助于在柏林建立一个风险指数，并在Berlin-Neukölln建立一个非常详细的社区空间水平。通过使用卫生办公室在Neukölln收集的高空间分辨率的已核实的COVID-19病例，将检查已确定的社区是否存在COVID-19疫情聚集性。

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.