Agent-based simulations of influenza spread are useful for decision making during public health emergencies. During such emergencies, decisions are required in cycles of less than day, and agent-based models should be adapted to support such decisions. The most important considerations for model adaptation are fast calibration of the model, low computational complexity as the population size is scaled up, and dependability of the results with low replication quantity. In previous work, we presented a self-calibrating model for agent-based influenza simulations. We now investigate whether general-purpose GPU computation is effective at accelerating the processing of this model to support health policy decision-making for pandemic and seasonal strains of the virus. The results of this paper indicate that a speedup of 94.3x is obtained with GPU algorithms for simulation sizes of 50 million people. Our GPU implementation scales linearly in the number of people which makes it a good choice for real-time decision support.

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