History matching with Gaussian-process emulation calibrates the Covasim COVID-19 agent-based model using about 5,300 runs instead of 100,000, producing posterior parameters that reproduce observed cases and deaths.
In this case, we used new random seeds during each history matching wave and for the final 50 posterior simulations
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Improving Policy-Oriented Agent-Based Modeling with History Matching: A Case Study
History matching with Gaussian-process emulation calibrates the Covasim COVID-19 agent-based model using about 5,300 runs instead of 100,000, producing posterior parameters that reproduce observed cases and deaths.