Utilization of spatially resolved data sources for an established agent-based model of Germany and its impact on predicted SARS-CoV-2 dynamics

利用空间解析数据源建立德国基于主体的模型及其对预测 SARS-CoV-2 动态的影响

• 批准号: 492390948
• 负责人: Professor Dr.-Ing. Bernd Hellingrath
• 金额: --
• 依托单位: Institut für Epidemiologie und Sozialmedizin
• 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 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/492390948?language=en
• 关键词: Utilization; spatially; resolved; data; sources

The goal of this project is to integrate real-time spatial health-, mobility- and behavioural data in a previously developed agent-based simulation platform to provide reliable regional forecasts of age-specific incidence rates for a period of 2-4 weeks at any stage of an epidemic. The model will be based on the agent-based simulation platform EPIPREDICT. The system already offers a comprehensive population model of the German population (approx. 80 million agents) on federal state, district and municipality levels. However, apart from the population structure, the model does currently not include any spatial information about simulated agents. While the platform has been assessed for its general usability in examining local infection dynamics and intervention strategies retrospectively, it has never been intended to provide regional short-term forecasts. These generally require a higher temporal and spatial resolution of input data. Due to its high spatial resolution the EPIPREDICT population model offers the opportunity to close this gap by integrating real-time spatial health-, mobility- and behavioural- data. With the present project proposal, we plan to extend the platform to include this perspective.For this purpose, four types of regional real-time data will be considered in the simulation, enabling regional short-term forecasts: the current pandemic situation, current mobility, current contact- and preventive behaviour, and current locally enforced non-pharmaceutical interventions (NPIs). Our three main project objectives are: (1) the development of a spatial agent-based forecasting model, (2) the development of a modelling workflow enabling efficient regular forecasts and (3) the development of a dashboard to make simulation results publicly available.The work program is divided into the two project areas. First, the "Data Management" project area led by the department of Epidemiology (André Karch) concerns the regular compilation, management, analysis, and preparation of data on the current infection dynamics to be integrated in the model. Second, the "Development" project area led by the department of Information Systems (Bernd Hellingrath) focusses on the model- and method development, dashboard- and interface development, as well as the execution and evaluation of forecasts. To achieve our goals regarding the processing of spatial data, the project team will be advised by the Institute for Geoinformatics of the University of Münster (Christian Kray) who takes a supporting role.Although we intend the model to be used in the context of the current pandemic, our findings and the prototype are applicable to support future pandemics and containment efforts. The modelling workflow proposed here can serve as a feasibility study for the development of a nationwide regional early warning system, which could be implemented in a follow-up project.

该项目的目标是将实时空间健康、流动性和行为数据纳入先前开发的基于代理的模拟平台，以便在流行病的任何阶段提供2至4周期间特定年龄发病率的可靠区域预测。该模型将基于基于代理的仿真平台EPIPREDICT。该系统已经提供了一个全面的德国人口模型（约。8000万代理人）在联邦州、区和市各级。然而，除了人口结构，该模型目前不包括任何空间信息的模拟代理。虽然该平台在回顾性地检查当地感染动态和干预战略方面的一般可用性得到了评估，但它从未打算提供区域短期预测。这些通常需要输入数据的更高的时间和空间分辨率。由于其高空间分辨率，EPIPREDICT人口模型提供了通过整合实时空间健康、流动和行为数据来缩小这一差距的机会。在目前的项目提案中，我们计划扩展该平台，以包括这一观点。为此，在模拟中将考虑四种类型的区域实时数据，从而实现区域短期预测：当前流行情况、当前流动性、当前接触和预防行为以及当前当地强制执行的非药物干预措施（NPI）。我们的三个主要项目目标是：（1）开发基于空间代理的预测模型，（2）开发能够实现高效定期预测的建模工作流程，以及（3）开发仪表板以公开模拟结果。工作计划分为两个项目区域。首先，由流行病学系（André Karch）领导的“数据管理”项目领域涉及定期汇编、管理、分析和准备将纳入模型的当前感染动态数据。其次，由信息系统部（Bernd Hellingrath）领导的“开发”项目领域侧重于模型和方法开发、仪表板和界面开发，以及预测的执行和评估。为了实现我们关于空间数据处理的目标，项目团队将由明斯特大学地理信息学研究所（Christian Kray）提供建议，该研究所将发挥支持作用。尽管我们打算将模型用于当前流行病的背景下，但我们的发现和原型适用于支持未来的流行病和遏制工作。这里提出的建模工作流程可以作为建立全国性区域预警系统的可行性研究，该系统可以在后续项目中实施。