When Do We Need Massive Computations to Perform Detailed COVID‐19 Simulations?. Issue 2 (23rd November 2021) - Lutz, Christopher B.; Giabbanelli, Philippe J.
Abstract: 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. Abstract : This paper analyzes machine learning meta‐models trained on a subset of COVID‐19 simulation data and compares the results predicted by these meta‐models to the full simulation dataset. For simulations with no strong interventions (e.g., vaccines or lockdowns) accurate meta‐models can be created with small amounts of training data. For simulations that use strong interventions, much more data is required.