Advanced Spatial Statistical Modelling of COVID-19 Data

COVID-19 数据的高级空间统计建模

• 批准号: 492351805
• 负责人: Professor Dr. Göran Kauermann
• 金额: --
• 依托单位: Institut für Statistik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2021
• 资助国家: 德国
• 起止时间: 2020-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/492351805?language=en
• 关键词: Advanced; Spatial; Statistical; Modelling; COVID

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.

COVID-19疫情分几个阶段发生。2020年春季的第一波疫情特点是重症监护室（ICU）入住压力大，PCR检测可用性有限，导致病例检出率低。与第一波相比，第二波显示老年人群的高发病率导致高死亡率和ICU床位需求增加。第一波和第二波都是以感染的强烈增加开始的。相比之下，2021年春季第三波疫情的特点是感染人数适度增加，死亡人数稳定，ICU床位占用率和住院时间分布发生变化，这是由年轻患者引起的。这三波疫情的不同特征说明了疫情的动态特征，具体体现在年龄别发病率、超额死亡率和ICU床位占用率等数据上。为了有效地控制大流行，需要对感染行为进行小规模的时空分析，并对遏制措施的有效性进行详细评估。然而，由于报告延迟、改变检测策略和引入强制性快速检测等问题，直接分析发病率数据是有问题的。类似的问题也出现在其他流行病指标的分析中。此外，由于同时执行不同的措施，而且数据会受到测量误差的影响，因此难以对遏制措施的有效性进行统计量化。我们应用先进的统计模型科普上述问题，以展示COVID-19疫情的区域模式，并使用次级小区域数据评估遏制措施对当地疫情的有效性。拟议的项目有两个目标：首先，我们研究社会经济、公共卫生和社交媒体数据与感染的相互作用，以探索和解释大流行病在德国各地区发展的方式和原因。该视图旨在了解三种波的不同模式，从而为未来的监视和早期热点检测提供更可靠的工具。其次，我们着眼于住院和ICU入住率，并开发一个模型，用于估计不同地区的ICU入院率。这使我们能够反映当地的感染动态，以及通过使用回归和变点模型来估计遏制措施对区域一级的影响。此外，这些模型可以用来建立一个短期的ICU入住率预测模型。