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

2020年3月，世界卫生组织宣布冠状病毒疾病的爆发为全球大流行。随着新冠肺炎疫情的爆发，除其他外，三个特征变得明显：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、社会和经济参数也在冠状病毒的传播中发挥作用。这在其他国家已经在个人层面上表现出来，但在德国还没有。2.新冠肺炎案件数量有明显的空间格局和底层过程。这些空间差异不仅在国家层面上可以观察到，而且在区域和地方层面上也是如此。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相关的问题的驱动力。柏林参议院卫生、护理和平等机会管理局在地区一级进行了初步分析，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。由于柏林地区在人口、社会结构和建筑环境方面非常不同，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面向世界的小规模生活区域系统(LOR，类似于居民区)的独特情况，市和区行政当局的大多数单位都使用该系统来汇总其数据。因此，这一大流行表明，有很大潜力改进工作流程和技术，以通过政策措施评估、分析、监测和适当处理这一大流行病(和潜在的其他健康)情况。该项目旨在开发和应用一套创新的时空数据分析技术，以在洛尔社区的详细空间水平上评估、分析和监测新冠肺炎大流行。我们使用来自数据库的行政数据，与卫生部关于冠状病毒大流行在柏林-Neukölln的Lor居民区一级传播的数据相关联。我们的目标是研究新冠肺炎的时空分布。我们认为，社会经济因素与新冠肺炎案件的分布之间存在有意义的联系。专门针对小地理单位的新冠肺炎案件及其与附近社会经济变量的联系的研究缺失。本研究试图结合新冠肺炎案例对小型行政单位的社会经济数据进行分析。这些发现有助于在柏林和--在非常详细的空间层面上--在柏林--Neukölln--开发一个风险指数。通过使用诺伊克伦卫生办公室收集的具有高空间分辨率的经核实的新冠肺炎病例，将检查确定的社区是否有聚集性的新冠肺炎疫情。

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.