Advanced Spatial Statistical Modelling of COVID-19 Data

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The COVID-19 pandemic took place in several phases. The first wave in spring 2020 was characterized by a high pressure on the occupancy of intensive care units (ICU) and limited availability of PCR tests resulting in low case detection rates. The second wave showed high incidence rates in the elderly population resulting in high mortality rates and an increased demand of ICU beds as compared to the first wave. Both, first and second wave started with a strong increase of infections. In contrast, the third wave in spring 2021 was characterized by a moderate increase of infections, stable mortality numbers, and a change in the occupancy and length-of-stay distribution of ICU beds, caused by younger patients. These different characteristics of the three waves illustrate the dynamic aspects of the pandemic, which are data-wise reflected in age-specific incidences, excess mortality, and ICU bed occupancy. For an effective pandemic control, small-scale spatio-temporal analyses of infection behavior are needed, together with a detailed evaluation of the effectiveness of containment measures. However, a straightforward analysis of incidence figures is problematic due to issues such as reporting delays, changing testing strategies, and the introduction of obligatory rapid tests. Similar problems also occur in the analyses of other pandemic indicators. In addition, the simultaneous implementation of different measures and the fact that the data are subject to the measurement errors make the statistical quantification of the effectiveness of containment measures difficult. We apply advanced statistical modelling to cope with the above-mentioned problems in order to exhibit regional patterns of the COVID-19 pandemic and to assess the effectiveness of containment measures on the local pandemic situation using secondary small-area data. The proposed project has two objectives: First, we take a look at the interplay of socio-economic, public health and social media data and infections to explore and explain how and why the course of the pandemic developed differently in German districts. This view aims to understand the different patterns of the three waves and thus provides a more reliable tool for future surveillance and early hotspot detection. Second, we look at hospitalizations and ICU occupancy and develop a model for estimating ICU admissions on different regional levels. This allows us to mirror local infection dynamics as well as to estimate the impact of containment measures on a regional level by using regression and changepoint models. Furthermore, these models can be used to build a short-term forecasting model of ICU occupancy.