Spatial-temporal Analysis of small-scale Determinants of the Covid-19 Pandemic

新冠肺炎在第二波和第三波的传播受到人口社会结构的强烈影响，发病率较高，特别是在较贫困的社区。在德国，迄今为止的空间分析使用县级数据来推断新冠肺炎的区域决定因素，但尚未调查城市邻里层面上的社会经济或人口集群的细节。该项目旨在填补这一空白。为此，我们将把社会经济、人口、城市和环境特征的综合空间信息与新冠肺炎疫情的时空聚合数据联系起来。基于对新冠肺炎传播的时空分析，该项目将使用地统计分析确定小尺度决定因素对周边地区的特定影响，并使用机器学习方法开发预测模型。更具体地说，我们将首先处理来自各种来源的时空数据，其中特别是关于新冠肺炎的数据将由位于联邦公共卫生办公室不来梅(Gesundheitsamt Bremet)的新冠肺炎危机股(CU)提供。新冠肺炎上的数据将提供不来梅市街道内两周时间段的统计数据，时间从2020年9月到2021年6月。将处理小范围区域层面的数据，包括社会经济和人口特征、建成环境、常见污染物、天气条件，特别是人口流动情况，以匹配新冠肺炎上的时空数据。移动性流动由电信网络数据评估，并将由Teralytics GmbH提供，以确定联邦或地方针对新冠肺炎的措施在移动性方面的变化。第二，将应用地统计工具检测新冠肺炎的时空集群。为了获得小尺度特征和新冠肺炎随时间的邻域特定关联的详细估计，将应用计算空间非平稳关联的局部地理加权回归(GWR)。第三，考虑到潜在的地区层面的决定因素，将实施机器学习技术来预测新冠肺炎在不来梅市社区的传播。该项目的预期成果将提供对新冠肺炎大流行的特定地区关键驱动因素以及特定社区联邦或地方对策的有效性的详细见解。这将支持不来梅地方当局制定具体的预防战略，考虑到弱势群体、城市结构和环境因素。在未来的项目中扩展数据处理和分析程序，以使用其他城市或整个国家可用的小规模区域级别的数据，将更准确地洞察特定区域级别的潜在目标，以抗击未来的流行病，甚至防止进一步爆发。

The spread of COVID-19 in the second and third wave was strongly affected by social structures in the population, with higher incidence rates particularly in more deprived neighbourhoods. In Germany, spatial analyses so far used county-level data to infer on area-level determinants of COVID-19, but details on socio-economic or demographic clusters on the neighbourhood-level within cities have not yet been investigated. This project aims to fill this gap. For this purpose, we will link a comprehensive set of spatial information on socio-economic, demographic, urban, and environmental characteristics with spatially and temporally aggregated data on the COVID-19 pandemic. Based on spatial-temporal analysis of the spread of COVID-19, this project will identify the neighbourhood-specific influence of small-scale determinants using geostatistical analyses and develop prediction models using machine learning methods. To be more specific, we will first process spatial-temporal data from a variety of sources, where especially data on COVID-19 will be provided by the COVID-19 Crisis Unit (CU) (Krisenstab Corona Bremen) located at the Federal Public Health Office Bremen (Gesundheitsamt Bremen). Data on COVID-19 will be provided for statistical neighbourhoods within sub-districts of the city of Bremen for time periods of two weeks from September 2020 to June 2021. Small-scale area-level data on socio-economic and demographic characteristics, built environment, common pollutants, weather conditions, and especially mobility flows of the population will be processed to match the spatial-temporal data on COVID-19. Mobility flows are assessed by telecom network data and will be provided by Teralytics GmbH to identify changes in mobility with respect to federal or local measures against COVID-19. Second, geostatistical tools will be applied to detect spatial-temporal clusters of COVID-19. To obtain detailed estimates of neighbourhood-specific associations of small-scale characteristics and COVID-19 over time, local geographically weighted regression (GWR) will be applied which calculates spatial non-stationary associations. Third, machine learning techniques will be implemented for the prediction of the spread of COVID-19 in neighbourhoods in the city of Bremen considering underlying area-level determinants. Expected results of this project will provide detailed insights into area-specific key drivers of the COVID-19 pandemic and neighbourhood-specific effectiveness of federal or local countermeasures. This will support local authorities of Bremen to develop specific prevention strategies accounting for vulnerable groups, urban structures, and environmental factors. Extending data processing and analysis procedures in future projects to use available small-scale area-level in other cities or in the entire country will yield more precise insights into potential targets on an area-specific level to combat future pandemics or even prevent further outbreaks.