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.

翻译：智能体模型（ABM）通过显式考虑多样化的个体交互与环境，为流行病学中的疫情爆发与干预措施建模提供了卓越框架。然而，这类模型通常具有随机性且参数维度高，需要精确校准以实现预测性能。当考虑实际规模的智能体数量并恰当处理随机性时，这种高维校准的计算成本可能过高。本文提出一种基于随机森林的代理建模技术以加速ABM评估，并演示了如何通过马尔可夫链蒙特卡洛（MCMC）方法校准名为CityCOVID的流行病学ABM。该技术首先结合CityCOVID的关注量（即住院与死亡人数）进行阐述，通过主成分分析（PCA）进行时间分解以实现降维，并辅以敏感性分析。随后提出校准问题，生成样本以最佳拟合2020年3月至6月芝加哥COVID-19住院与死亡数据。这些结果与先前近似贝叶斯校准（IMABC）结果进行比较，并通过预测性能分析表明该方法在降低计算量的同时提升了性能。