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

该项目的目标是将实时空间健康、流动性和行为数据整合到以前开发的基于主体的模拟平台中，以在流行病的任何阶段提供2-4周内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。该系统已经为德国人口提供了一个全面的人口模型。8000万名特工)在联邦、州、区和市各级工作。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。虽然该平台已被评估为在回顾性检查当地感染动态和干预策略方面的一般可用性，但它从未打算提供区域短期预测。这些通常需要更高的输入数据的时间和空间分辨率。由于其高空间分辨率，EPIPREDICT人口模型通过整合实时空间健康、流动性和行为数据，为缩小这一差距提供了机会。在目前的项目提案中，我们计划扩展平台以包含此视角。为此目的，模拟中将考虑四种区域实时数据，以便进行区域短期预测：当前的大流行病情况、当前的流动性、当前的接触和预防行为，以及当前当地执行的非药物干预措施。我们的三个主要项目目标是：(1)开发一个基于空间主体的预测模型，(2)开发一个建模工作流，实现有效的定期预测，(3)开发一个仪表板，使模拟结果公开可用。工作计划分为两个项目区。首先，由流行病学系（andr<s:1> Karch）领导的“数据管理”项目领域涉及定期汇编、管理、分析和准备有关当前感染动态的数据，并将其整合到模型中。其次，由信息系统部门（Bernd hellingath）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。为了实现我们在空间数据处理方面的目标，项目团队将由德国<s:1> nster大学地理信息学研究所（Christian Kray）提供建议，并担任支持角色。虽然我们打算在当前大流行的背景下使用该模型，但我们的研究结果和原型适用于支持未来的大流行和遏制工作。本文提出的建模工作流程可作为开发全国性区域预警系统的可行性研究，并可在后续项目中实施。

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.