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Accelerating Bayesian inference for stochastic epidemic models using incidence data

Lookup NU author(s): Dr Andrew Golightly, Dr Laura WadkinORCiD, Sam Whitaker, Dr Andrew BaggaleyORCiD, Professor Nick ParkerORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


We consider the case of performing Bayesian inference for stochastic epidemic compartment models, using incomplete time course data consisting of incidence counts that are either the number of new infections or removals in time intervals of fixed length. We eschew the most natural Markov jump process representation for reasons of computational efficiency, and focus on a stochastic differential equation representation. This is further approximated to give a tractable Gaussian process, that is, the linear noise approximation (LNA). Unless the observation model linking the LNA to data is both linear and Gaussian, the observed data likelihood remains intractable. It is in this setting that we consider two approaches for marginalising over the latent process: a correlated pseudo-marginal method and analytic marginalisation via a Gaussian approximation of the observation model. We compare and contrast these approaches using synthetic data before applying the best performing method to real data consisting of removal incidence of oak processionary moth nests in Richmond Park, London. Our approach further allows comparison between various competing compartment models.

Publication metadata

Author(s): Golightly A, Wadkin LE, Whitaker SA, Baggaley AW, Parker NG, Kypraios T

Publication type: Article

Publication status: Published

Journal: Statistics and Computing

Year: 2023

Volume: 33

Online publication date: 12/10/2023

Acceptance date: 26/09/2023

Date deposited: 20/10/2023

ISSN (print): 0960-3174

ISSN (electronic): 1573-1375

Publisher: Springer Nature


DOI: 10.1007/s11222-023-10311-6


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Funder referenceFunder name
EPSRC New Horizons Grant
NERC Knowledge Exchange Fellows Grant