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

于二零二零年三月，世卫组织宣布冠状病毒病爆发为全球大流行病。随着COVID-19疫情的爆发，其中三个特点变得明显：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作用。这在其他国家的个人水平上得到了证明，但在德国还没有。2. COVID-19病例的数量存在明显的空间模式和潜在过程。这些空间差异不仅在国家一级，而且在区域和地方一级都可以观察到。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力。柏林参议院卫生、护理和平等机会管理局在地区层面进行了初步分析，以显示2020年夏末新冠肺炎大流行的一些决定因素的统计意义。由于柏林各区在人口、社会结构和建筑环境方面差异很大，我们认为各区的差异还不足以进行可靠的分析。该项目将利用柏林的独特情况，提供面向生活世界的小规模区域（LOR，类似于街区）系统，城市和地区行政部门的大多数单位都使用该系统来汇总数据。因此，这一大流行病表明，在改进工作流程和技术以评估、分析、监测和充分处理这一大流行病（和潜在的其他健康）情况方面，有很大的潜力，可以采取政策措施。该项目旨在开发和应用一套创新的时空数据分析技术，以在LOR社区的详细空间层面上评估、分析和监测COVID-19疫情。我们使用数据库中的行政数据与卫生部门关于柏林-新克尔恩LOR社区层面冠状病毒大流行传播的数据。我们的目标是调查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。缺乏专门针对小地理单元的COVID-19病例及其与社区社会经济变量的联系的研究。这项研究旨在分析小型行政单位的COVID-19病例的社会经济数据。研究结果有助于制定一个风险指数在柏林和-在一个非常详细的空间层次的邻里-在柏林-新克尔恩。通过使用新克尔恩卫生办公室收集的高空间分辨率的经验证的COVID-19病例，将检查已确定的社区是否存在COVID-19疫情集群。

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.