Capturing the time-varying drivers of an epidemic using stochastic dynamical systems

TL;DR

This paper introduces stochastic dynamical systems with diffusion processes for modeling time-varying epidemic drivers, using adaptive particle MCMC for inference, validated on simulated data and applied to the 2009 H1N1 pandemic in England.

Contribution

It presents a novel stochastic modeling approach with diffusion processes for epidemic parameters and an adaptive particle MCMC inference method, applied to real-world pandemic data.

Findings

Effective contact rate trajectories estimated in real time.

Validated computational methods on simulated data.

Applied model provides insights for public health decisions.

Abstract

Epidemics are often modelled using non-linear dynamical systems observed through partial and noisy data. In this paper, we consider stochastic extensions in order to capture unknown influences (changing behaviors, public interventions, seasonal effects etc). These models assign diffusion processes to the time-varying parameters, and our inferential procedure is based on a suitably adjusted adaptive particle MCMC algorithm. The performance of the proposed computational methods is validated on simulated data and the adopted model is applied to the 2009 H1N1 pandemic in England. In addition to estimating the effective contact rate trajectories, the methodology is applied in real time to provide evidence in related public health decisions. Diffusion driven SEIR-type models with age structure are also introduced.