Below you can find a list of projects related to IMMIDD. 

Click on the titles to read more.

• Epipredict

  Participating institutes: 
  Institute of Epidemiology and Social Medicine, Department of Information Systems and Institute of Molecular Virology

   

  The COVID-19 pandemic highlighted the impact of emerging infectious diseases on various aspects of public life. Decision-makers in the public-health sector faced the challenge of selecting effective countermeasures for a newly emerging disease with limited historical data and little understanding of its dynamics. To evaluate these decisions, infectious disease modeling has proven to be a valuable tool, providing insights into disease dynamics and predicting future outcomes for different scenarios. Agent-based models, which simulate populations at an individual level, are especially well-suited to capture the complex individual behaviors and the arising aggregated system evolution, making these models suitable tools to evaluate disease progression within highly heterogeneous populations. This paper focuses on the EpiPredict project, which has aimed to develop a flexible, easy-to-use simulation framework for constructing, executing, and analyzing agent-based infectious disease models. The project objective arose from the observation that epidemiologists or public-health decision-makers, i.e., people without a strong IT background, lacked simulation tools, as most available tools required extensive programming skills to create and simulate agent-based models. Within this paper, the EpiPredict project and platform will be presented, and the relation of agents to the field of artificial intelligence discussed.

• OptimAgent

  Participating institutes: 
  Institute of Epidemiology and Social Medicine and Department of Information Systems

  Optimal control of the epidemic under heterogeneity conditions – decision making perspective on agent based modelling (OptimAgent)

  OptimAgent aims to develop a standardized framework for decision-making during a pandemic based on a dedicated agent-based mathematical model tailored to reflect the German population. The specific focus is on the conceptualization of the model, including expertise from various disciplines and a realistic set-up accounting for heterogeneities in the population structure, intra- and inter-individual contacts, mobility, individual sociological and psychological characteristics and linking epidemiological outcomes to a public health decision-making framework. This covers health economic analyses of direct outcomes and effects of nonpharmaceutical interventions (NPIs) on society. The agent-based approach is chosen to allow direct implementation of all NPIs applied during the various stages of the SARS-CoV-2 epidemic and other potential control strategies. The effects of heterogeneity will be studied to identify dimensions of heterogeneity of particular importance, informing the potential need for additional data collection in the case of a new pandemic and providing estimates reducing the uncertainty for decision-makers. The model will use a modular structure, allowing the adaptation to new pathogen characteristics in the future, or novel interventions (e.g. treatment reducing infectiousness), if such become available.

  Furthermore, it will implement changes from introducing new genotypes and an elaborated parameter estimation tool based on Bayesian statistics. The intensive consultation process, including international experts, should ensure acceptance and provide validation against common standards. While the model's focus is on Germany, it should further our understanding of epidemics in general and join the modelling efforts in other countries. The broad interaction of experts from various disciplines is necessary to reproduce the real complexity in the system and allow for an informed decision-making perspective providing information about the cost-effectiveness of alternative strategies.

  The project is divided into 6 subprojects. Our work is on subproject 1 which the overall aims are:

  1) to understand in more detail the intrinsic heterogeneity and dynamics of contact network structures based on detailed analyses of pre-pandemic and pandemic contact data and

  2) to assess the effects of using a more realistic contact network on the results of dynamic mathematical models at different stages of an epidemic of airborne infectious disease.

• SpaceImpact

  Participating institutes: 
  Institute of Epidemiology and Social Medicine and Department of Information Systems

  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.