The SARS-CoV-2 pandemic poses an unprecedented challenge to society and policy-making. The goal of OptimAgent is to develop a standardized model-based framework to support public health decision-making processes. This framework will enable the evaluation of a wide range of infection control interventions. The focus is on the design of an agent-based model that goes significantly beyond the simulation approaches available to date.
Through a flexible modular structure and an extensive consultation process with national and international modeling experts, the model will be optimized to inform health policy decision-making during future pandemics. Furthermore, it will be adaptable to endemic pathogens and will realistically reflect the socio-demographic and regional structures of Germany. The agents underlying the model combine demographic, socioeconomic, sociological, and psychological characteristics that influence individual contact behavior, risk of infection, and risk of disease.
Based on the results of comprehensive and sophisticated analyses of contact behavior, specific model modules on selective, targeted contact restraints in different settings, contact tracking and testing strategies are developed. The flexible modular design of the model also offers possibilities for easy integration of additional components.
The main focus of the project is to analyze the impact of different aspects of heterogeneity in the population structure and their interaction on the incidence of infection. This will provide new insights regarding the role of heterogeneity in the spread of severe respiratory infectious diseases in the population and the effectiveness of pandemic control measures.