Bayesian Calibration of Stochastic Agent Based Model via Random Forest
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Wiley Open Access
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| Formato: | Artículo Open Access |
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Wiley
2025
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| _version_ | 1867010563078356992 |
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| author | Connor Robertson Cosmin Safta Nicholson Collier Jonathan Ozik Jaideep Ray |
| author_facet | Connor Robertson Cosmin Safta Nicholson Collier Jonathan Ozik Jaideep Ray Connor Robertson Cosmin Safta Nicholson Collier Jonathan Ozik Jaideep Ray |
| collection | Wiley Open Access |
| contents | Bayesian Calibration of Stochastic Agent Based Model via Random Forest Connor Robertson Cosmin Safta Nicholson Collier Jonathan Ozik Jaideep Ray Statistics in Medicine ABSTRACTAgent‐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. 10.1002/sim.70029 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/sim.70029 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_70029 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Bayesian Calibration of Stochastic Agent Based Model via Random Forest Connor Robertson Cosmin Safta Nicholson Collier Jonathan Ozik Jaideep Ray Statistics in Medicine Bayesian Calibration of Stochastic Agent Based Model via Random Forest Connor Robertson Cosmin Safta Nicholson Collier Jonathan Ozik Jaideep Ray Statistics in Medicine ABSTRACTAgent‐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. 10.1002/sim.70029 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Bayesian Calibration of Stochastic Agent Based Model via Random Forest |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.70029 |