The COVID‐19 pandemic has infected over 250 million people worldwide and killed more than 5 million as of November 2021. Many intervention strategies are utilized (e.g., masks, social distancing, vaccinations), but officials making decisions have a limited time to act. Computer simulations can aid them by predicting future disease outcomes, but they also require significant processing power or time. It is examined whether a machine learning model can be trained on a small subset of simulation runs to inexpensively predict future disease trajectories resembling the original simulation results. Using four previously published agent‐based models (ABMs) for COVID‐19, a decision tree regression for each ABM is built and its predictions are compared to the corresponding ABM. Accurate machine learning meta‐models are generated from ABMs without strong interventions (e.g., vaccines, lockdowns) using small amounts of simulation data: the root‐mean‐square error (RMSE) with 25% of the data is close to the RMSE for the full dataset (0.15 vs 0.14 in one model; 0.07 vs 0.06 in another). However, meta‐models for ABMs employing strong interventions require much more training data (at least 60%) to achieve a similar accuracy. In conclusion, machine learning meta‐models can be used in some scenarios to assist in faster decision‐making.

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The COVID-19 pandemic has infected over 200 million people worldwide and killed more than 4 million as of August 2021. Many intervention strategies have been utilized by governments around the world, including masks, social distancing, and vaccinations. However, officials making decisions regarding interventions may have a limited time to act. Computer simulations can aid them by predicting future disease outcomes, but they also have limitations due to requirements on processing power or time. This paper examines whether a machine learning model can be trained on a small subset of simulation runs to inexpensively predict future disease trajectories very close to the original simulation results. Using four previously published agent-based models for COVID-19, this paper analyzes the predictions of decision tree regression machine learning models and compares them to the results of the original simulations. The results indicate that accurate machine learning meta-models can be generated from simulation models with no strong interventions (e.g., vaccines, lockdowns) using small amounts of simulation data. However, meta-models for simulation models that include strong interventions required much more training data to achieve a similar accuracy. This indicates that machine learning meta-models could be used in some scenarios to assist in faster decision making.