Advancing calibration for stochastic agent-based models in epidemiology with Stein variational inference and Gaussian process surrogates

TL;DR

This paper explores the use of Stein Variational Inference with Gaussian process surrogates as a scalable and efficient alternative to traditional MCMC methods for calibrating complex stochastic agent-based epidemiological models, maintaining accuracy while reducing computational costs.

Contribution

It introduces a novel application of SVI combined with GP surrogates for calibrating high-dimensional stochastic ABMs in epidemiology, demonstrating comparable performance to MCMC.

Findings

SVI achieves similar predictive accuracy to MCMC.

SVI offers improved scalability and efficiency for high-dimensional models.

Practical challenges include hyperparameter tuning and particle monitoring.

Abstract

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.