Bayesian calibration of stochastic agent based model via random forest
This work addresses the challenge of efficiently calibrating complex epidemiological models for better outbreak predictions, though it is incremental as it builds on existing surrogate modeling and calibration methods.
The paper tackles the computationally prohibitive calibration of stochastic, high-dimensional agent-based models in epidemiology by introducing a random forest surrogate modeling technique, demonstrating improved predictive performance and reduced computation time when calibrating the CityCOVID model to match COVID-19 hospitalization and death data in Chicago from March to June 2020.
Agent-based models (ABM) provide an excellent framework for modeling outbreaks and interventions in epidemiology by explicitly accounting for diverse individual interactions and environments. However, these models are usually stochastic and highly parametrized, requiring precise calibration for predictive performance. When considering realistic numbers of agents and properly accounting for stochasticity, this high dimensional calibration can be computationally prohibitive. This paper presents a random forest based surrogate modeling technique to accelerate the evaluation of ABMs and demonstrates its use to calibrate an epidemiological ABM named CityCOVID via Markov chain Monte Carlo (MCMC). The technique is first outlined in the context of CityCOVID's quantities of interest, namely hospitalizations and deaths, by exploring dimensionality reduction via temporal decomposition with principal component analysis (PCA) and via sensitivity analysis. The calibration problem is then presented and samples are generated to best match COVID-19 hospitalization and death numbers in Chicago from March to June in 2020. These results are compared with previous approximate Bayesian calibration (IMABC) results and their predictive performance is analyzed showing improved performance with a reduction in computation.