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Accelerating Bayesian inference for stochastic epidemic models using incidence data

A Golightly, LE Wadkin, S Whitaker, A Baggaley… - arXiv preprint arXiv …, 2023 - arxiv.org
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… In such situations, we envisage that our approach is likely to be of most practical benefit in providing initial values and tuning choices for simulation-based inference schemes that target …

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@article{2303.15371v2,
Author        = {Andrew Golightly and Laura E. Wadkin and Sam A. Whitaker and Andrew W. Baggaley and Nick G. Parker and Theodore Kypraios},
Title         = {Accelerating Bayesian inference for stochastic epidemic models using
  incidence data},
Eprint        = {2303.15371v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.CO},
Abstract      = {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.},
Year          = {2023},
Month         = {Mar},
Url           = {http://arxiv.org/abs/2303.15371v2},
File          = {2303.15371v2.pdf}
}

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