Conducting a Dynamic Microsimulation for Care Research: Data Generation, Transition Probabilities and Sensitivity Analysis

This contribution provides insights on a novel dynamic microsimulation model that is developed within the research project Regionale Mikrosimulationen und Indikatorsysteme (REMIKIS). It facilitates multivariate analyses of long-term care demand and supply structures in the city of Trier while accounting for its infrastructural characteristics as well as social dependencies of its citizens. The implementation requires three major steps and the combination of multiple data sources. The first step is the generation of a base population based on census grid data. Census totals, survey data, and OpenStreetMap information are combined to create a realistic distribution of artificial units from empirical parameters and geo-referenced addresses. The second step is the dynamic projection of the base population via stochastic processes. For this, empirical models for transition probability estimation from surveys like the German Socioeconomic Panel and the German Microcensus are used. The third step is a sensitivity analysis of the simulation outcomes with respect to the scenarios of the simulation modules. This enables the identification of genuine effects and dependencies throughout the simulation. We provide descriptions of all steps and the required data usage. Further, some first results from REMIKIS are presented.