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Lookup NU author(s): Dr Andrew Golightly, Dr Laura Wadkin, 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.
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
URL: https://doi.org/10.1007/s11222-023-10311-6
DOI: 10.1007/s11222-023-10311-6
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