Evaluating Parallelization Strategies for Large-Scale Individual-based Infectious Disease Simulations.

Individual-based models (IBMs) of infectious disease dynamics with full-country populations often suffer from high runtimes. While there are approaches to parallelize simulations, many prominent epidemic models exhibit single-core implementations, suggesting a lack of consensus among the research community on whether parallelization is desirable or achievable. Rising demands in model scope and complexity, however, imply that performance will continue to be a bottleneck. In this paper, we discuss the requirements and challenges of parallel IBMs in general and the German Epidemic Micro-Simulation System (GEMS) in particular. While the exploitation of unique model characteristics can yield significant performance improvement potential, parallelization strategies generally necessitate trade-offs in either hardware requirements, model fidelity, or implementation complexity. Therefore, the selection of parallelization strategies requires a comprehensive assessment. We present a point-based evaluation scheme to assess the potential of parallelization strategies as our main contribution and exemplify its application in the context of GEMS.