Generating synthetic populations based on german census data
Ponge Johannes, Enbergs Malte, Schüngel Michael, Hellingrath Bernd, Karch André, Ludwig Stephan
Spatial agent-based simulations of infectious disease epidemics require a high-resolution regional population model. However, only aggregated demographic data is available for most geographic regions. Furthermore, the infectious disease application case can require the fusion of multiple data sources (e.g. census and public health statistics), inducing demand for a modular and extensible modeling approach. In this work we provide a novel sequential sample-free approach to generate synthetic baseline populations for agent-based simulations, combining synthetic reconstruction and combinatorial optimization. We applied the approach to generate a population model for the German state of North Rhine-Westphalia (17.5 million inhabitants) which yielded an average accuracy of around 98% per attribute. The resulting population model is publicly available and has been utilized in multiple simulation-based infectious disease case studies. We suggest that our research can pave the way for more geographically granular synthetic populations to be used in model-driven infectious disease epidemics prediction and prevention.