A computationally efficient framework for realistic epidemic modelling through Gaussian Markov random fields

TL;DR

This paper introduces a Bayesian framework using Gaussian Markov random fields for efficient epidemic modeling that accounts for environmental stochasticity, improving nowcasting and forecasting of infectious disease outbreaks.

Contribution

It presents a novel, computationally efficient Bayesian method employing Gaussian Markov random fields to model environmental stochasticity in epidemic processes.

Findings

Effective modeling of environmental factors like seasonality and social cycles.

Accurate epidemic nowcasting and forecasting demonstrated on COVID-19 UK data.

Scalable inference with a new MCMC algorithm.

Abstract

We tackle limitations of ordinary differential equation-driven Susceptible-Infections-Removed (SIR) models and their extensions that have recently be employed for epidemic nowcasting and forecasting. In particular, we deal with challenges related to the extension of SIR-type models to account for the so-called \textit{environmental stochasticity}, i.e., external factors, such as seasonal forcing, social cycles and vaccinations that can dramatically affect outbreaks of infectious diseases. Typically, in SIR-type models environmental stochasticity is modelled through stochastic processes. However, this stochastic extension of epidemic models leads to models with large dimension that increases over time. Here we propose a Bayesian approach to build an efficient modelling and inferential framework for epidemic nowcasting and forecasting by using Gaussian Markov random fields to model the evolution of these stochastic processes over time and across population strata. Importantly, we also develop a bespoke and computationally efficient Markov chain Monte Carlo algorithm to estimate the large number of parameters and latent states of the proposed model. We test our approach on simulated data and we apply it to real data from the Covid-19 pandemic in the United Kingdom.