# Bayesian calibration of stochastic agent based model via random forest

arXiv: https://arxiv.org/abs/2406.19524 · HTML: https://mlsanity.com/paper/2406.19524/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 63 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC](https://mlsanity.com/paper/1507.04544/) (2015)
- [Automatic Differentiation of Programs with Discrete Randomness](https://mlsanity.com/paper/2210.08572/) (2022)
- [Differentiable Agent-based Epidemiology](https://mlsanity.com/paper/2207.09714/) (2022)
- [Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates](https://mlsanity.com/paper/2010.06558/) (2020)
- [Interventionally Consistent Surrogates for Agent-based Simulators](https://mlsanity.com/paper/2312.11158/) (2023)
- [Microsimulation Model Calibration using Incremental Mixture Approximate Bayesian Computation](https://mlsanity.com/paper/1804.02090/) (2018)
- [Evaluating Forecasts with scoringutils in R](https://mlsanity.com/paper/2205.07090/) (2022)
- [Variational Inference of Parameters in Opinion Dynamics Models](https://mlsanity.com/paper/2403.05358/) (2024)
- [Variational Inference with Agent-Based Models](https://mlsanity.com/paper/1605.04360/) (2016)
- [Trajectory-oriented optimization of stochastic epidemiological models](https://mlsanity.com/paper/2305.03926/) (2023)
- [Inferring epidemic dynamics using Gaussian process emulation of agent-based simulations](https://mlsanity.com/paper/2307.12186/) (2023)

## Built on by (most cited first, 4 of 4)

- [Graph Neural Network Surrogates to leverage Mechanistic Expert Knowledge towards Reliable and Immediate Pandemic Response](https://mlsanity.com/paper/2411.06500/) (2024)
- [Advancing calibration for stochastic agent-based models in epidemiology with Stein variational inference and Gaussian process surrogates](https://mlsanity.com/paper/2502.19550/) (2025)
- [ABM-UDE: Developing Surrogates for Epidemic Agent-Based Models via Scientific Machine Learning](https://mlsanity.com/paper/2602.21588/) (2026)
- [Machine learning-assisted calibration of Agent-based Models: surrogate-based optimization with Genetic Algorithm and Particle Swarm Optimization](https://mlsanity.com/paper/2609.13247/) (2026)

## Abstract (arXiv)

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.