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.