Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal

TL;DR

This paper introduces a gradient-based Particle-MCMC method using NUTS for more efficient and accurate Bayesian inference of stochastic disease transmission models, outperforming traditional Metropolis-Hastings approaches.

Contribution

It develops a novel gradient-informed proposal method for Particle-MCMC, improving inference speed and accuracy for stochastic SEIR and SIR models.

Findings

NUTS-based proposals yield more accurate parameter estimates.

The new method converges faster than Metropolis-Hastings.

Enhanced inference of disease transmission parameters.

Abstract

State-space models have been widely used to model the dynamics of communicable diseases in populations of interest by fitting to time-series data. Particle filters have enabled these models to incorporate stochasticity and so can better reflect the true nature of population behaviours. Relevant parameters such as the spread of the disease, $R_t$, and recovery rates can be inferred using Particle MCMC. The standard method uses a Metropolis-Hastings random-walk proposal which can struggle to reach the stationary distribution in a reasonable time when there are multiple parameters. In this paper we obtain full Bayesian parameter estimations using gradient information and the No U-Turn Sampler (NUTS) when proposing new parameters of stochastic non-linear Susceptible-Exposed-Infected-Recovered (SEIR) and SIR models. Although NUTS makes more than one target evaluation per iteration, we show that it can provide more accurate estimates in a shorter run time than Metropolis-Hastings.