OptimAgent

Optimized strategies for epidemic control in highly heterogeneous populations –
A decision analytic approach to agent-based modeling

Project content and objectives

The SARS-CoV-2 pandemic represents an unprecedented challenge for society and political decision-making. The aim of OptimAgent is to develop a standardized model-based framework to support decision-making processes in the field of public health. This framework enables the evaluation of a wide range of infection control measures. The focus is on the design of an agent-based model that goes far beyond the simulation approaches available to date.
A flexible modular structure and a comprehensive consultation process with national and international modelling experts will optimize the model in particular to provide informative support for health policy decision-making processes during future pandemics.

In addition, it will be adaptable for endemic pathogens and realistically reflect the socio-demographic and regional structures of Germany. The agents on which the model is based combine demographic, socioeconomic, sociological and psychological characteristics that influence individual contact behavior, infection and disease risk.
Based on the results of comprehensive and differentiated analyses of contact behavior, specific model modules for selective, targeted contact restrictions in different settings, contact tracing and testing strategies are developed. The flexible modular model structure also offers the option of easily integrating additional components.

The focus of the project is to analyze the effects of different aspects of the heterogeneity of the population structure and their interaction on the incidence of infection. The aim is to gain new insights into the significance of heterogeneity in the spread of severe respiratory infectious diseases in the population and the effectiveness of pandemic control measures.

Project structure

The OptimAgent project is divided into the phases “Conceptualization”, “Development & Analysis” and “Application” and comprises six interlinked sub-projects.

Subproject 1 – Heterogeneity in contact behavior and in the use of prevention measures in different phases of an epidemic

Examines heterogeneity in contact behavior and compliance with non-pharmaceutical interventions and their impact on transmission patterns in the population

Subproject 2 – Heterogeneity of the socio-psychological determinants of health behavior

Focuses on psychosocial aspects that influence health behavior and creates a module to generate artificial populations

Subproject 3 – Estimation of spatial and temporal heterogeneity – Methods for parameterization and model training

Develops a principled approach to derive time- and location-resolved epidemiological parameters from different data sources

Subproject 4 – Generation of representative scenario populations for epidemiological models

Creates a tool to generate representative scenario populations for epidemiological models

Subproject 5 – Development of the German Epidemic Micro-Simulation System

Will develop an agent-based reference model for Germany that integrates work from subprojects 1-4 and 6 and enables the simulation of complex scenarios.

Subproject 6 – Effects of synergistic interactions between exposure and susceptibility to infectious diseases on socioeconomic inequalities in disease burden and the effects of infection control measures

Creates a decision analytic module that assesses the effectiveness and efficiency of alternative non-pharmaceutical interventions.

Project leaders and partners

Overall project: Prof. Dr. Rafael Mikolajczyk, Martin Luther University Halle-Wittenberg

Subproject 1

Prof. Dr. André Karch (subproject leader), University Münster
Dr. Veronika Jäger, University Münster
Phuong Huynh, University Münster
Prof. Dr. Rafael Mikolajczyk, Martin Luther University Halle-Wittenberg
Chao Xu, Martin Luther University Halle-Wittenberg
Prof. Dr. Vitaly Belik, Free University of Berlin
Dr. Andrzej K. Jarynowski, Free University of Berlin
Dr. Steven Schulz, NET CHECK
Richard Pastor, NET CHECK

Subproject 2

Prof. Dr. André Calero Valdez (subproject leader), University of Lübeck
Lilian Kojan, University of Lübeck

Subproject 3

Prof. Dr. Markus Scholz (subproject leader), University Leipzig

Subproject 4

Prof. Dr. Jan Pablo Burgard (subproject leader), University of Trier
Prof. Dr. Ralf Münnich, University Trier
Soheil Shams, University Trier
João Vitor Pamplona, University Trier

Subproject 5

Dr. Wolfgang Bock (subproject leader), Linnaeus University
Dr. Sudarshan Tiwari, University of Kaiserslautern-Landau
Dr. Bernd Hellingrath, University Münster
Johannes Ponge, University Münster
Janik Suer, University Münster
Dr. Johannes Horn, Martin Luther University Halle-Wittenberg
Prof. Dr. Mirjam E. Kretzschmar, University Medical Center Utrecht
Prof. Dr. Tyll Krüger, Wroclaw University of Science and Technology

Subproject 6

Jun.-Prof. Dr. Alexander Kuhlmann (subproject leader), Martin Luther University Halle-Wittenberg
Dr. Berit Lange, Helmholtz Centre for Infection Research
Dr. Isti Rodiah, Helmholtz Centre for Infection Research
Prof. Dr. Wolfgang Greiner, Bielefeld University
Maren Steinmann, Bielefeld University
Sebastian Gruhn, Bielefeld University
Dr. Beate Jahn, UMIT Tirol – Private University for Health Sciences and Health Technology
Prof. Dr. Uwe Siebert, UMIT Tirol – Private University for Health Sciences and Health Technology

Scientific Advisory Board

Vittoria Colizza, Head of Research at the Pierre Louis Institute of Epidemiology

Luca Ferretti, Senior Researcher at the Big Data Institute, University of Oxford

Simon Cauchemez, Head of the Department of Mathematical Modeling of Infectious Diseases at the Institut Pasteur

Stefan Flasche, Co-Director of the Centre for Mathematical Modelling of Infectious Diseases (CMMID), London School of Hygiene and Tropical Medicine

Franciszek Rakowski.
Head of the Polish modeling team, ICM University of Warsaw

Nicolas Popper, Chairman of DEXHELPP (Decision Support for Health Policy and Planning), Coordinator of COCOS (Centre for Computational Complex Systems), Vienna University of Technology