Accurate calibration of stochastic agent-based models (ABMs) in epidemiology is crucial to make them useful in public health policy decisions and interventions. Traditional calibration methods, e.g., Markov Chain Monte Carlo (MCMC), that yield a probability density function for the parameters being calibrated, are often computationally expensive. When applied to ABMs which are highly parametrized, the calibration process becomes computationally infeasible. This paper investigates the utility of Stein Variational Inference (SVI) as an alternative calibration technique for stochastic epidemiological ABMs approximated by Gaussian process (GP) surrogates. SVI leverages gradient information to iteratively update a set of particles in the space of parameters being calibrated, offering potential advantages in scalability and efficiency for high-dimensional ABMs. The ensemble of particles yields a joint probability density function for the parameters and serves as the calibration. We compare the performance of SVI and MCMC in calibrating CityCOVID, a stochastic epidemiological ABM, focusing on predictive accuracy and calibration effectiveness. Our results demonstrate that SVI maintains predictive accuracy and calibration effectiveness comparable to MCMC, making it a viable alternative for complex epidemiological models. We also present the practical challenges of using a gradient-based calibration such as SVI which include careful tuning of hyperparameters and monitoring of the particle dynamics.

翻译：流行病学中随机智能体模型（ABMs）的精确标定对于使其在公共卫生政策决策与干预中发挥实际作用至关重要。传统标定方法（如马尔可夫链蒙特卡洛方法）虽能生成待标定参数的概率密度函数，但通常计算成本高昂。当应用于高度参数化的ABMs时，标定过程在计算上变得不可行。本文研究了Stein变分推断（SVI）作为一种替代标定技术的实用性，该方法通过高斯过程（GP）代理模型对随机流行病学ABMs进行近似。SVI利用梯度信息迭代更新待标定参数空间中的一组粒子，为高维ABMs提供了可扩展性与效率方面的潜在优势。该粒子集合生成参数的联合概率密度函数并作为标定结果。我们在标定随机流行病学ABM模型CityCOVID时，对比了SVI与MCMC在预测精度与标定效果方面的性能。结果表明，SVI在保持与MCMC相当的预测精度与标定效果的同时，成为复杂流行病学模型的可行替代方案。我们还讨论了使用基于梯度的标定方法（如SVI）面临的实际挑战，包括超参数的精细调优与粒子动态的监测。