Parallelization of large-scale agent-based epidemiological simulations

Abstract

Agent-based epidemiological models have been applied widely successfully during the SARS-CoV-2 pandemic and assisted policymakers in assessing the effectiveness of intervention strategies. The computational complexity of agent-based models is still challenging, and therefore it is important to utilize modern multi-core systems as good as possible. In this paper, we are presenting our work on parallelizing the epidemiological simulation model MATSim Episim. Episim combines a large-scale person-centric human mobility model with a mechanistic model of infection and a person-centric disease progression model. In general, the parallelization of agent-based models with an inherent sequential structure — in the case of epidemiological models, the temporal order of the individual movements of the agents — is challenging. Especially when the underlying social network is irregular and dynamic, they require frequent communication between the processing elements. In Episim, however, we were able to take advantage of the fact that people are not contagious on the same day they become infected, and therefore immediate health synchronization is not required. By parallelizing some of the most computationally intensive submodels, we are now able to run MATSim Episim simulations up to eight times faster than the serial version. This makes it feasible to increase the number of agents, e.g. to run simulations for the whole of Germany instead of just Berlin as before. © 2022 The Authors.