Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal

Conor Rosato, John Harris, Jasmina Panovska-Griffiths, Simon Maskell

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    State-space models have been widely used to model the dynamics of communicable diseases in populations of interest by fitting to time-series data. Particle filters have enabled these models to incorporate stochasticity and so can better reflect the true nature of population behaviours. Relevant parameters such as the spread of the disease, Rt, and recovery rates can be inferred using Particle MCMC. The standard method uses a Metropolis-Hastings random-walk proposal which can struggle to reach the stationary distribution in a reasonable time when there are multiple parameters. In this paper we obtain full Bayesian parameter estimations using gradient information and the No U-Turn Sampler (NUTS) when proposing new parameters of stochastic non-linear Susceptible-Exposed-Infected-Recovered (SEIR) and SIR models. Although NUTS makes more than one target evaluation per iteration, we show that it can provide more accurate estimates in a shorter run time than Metropolis-Hastings.

    Original languageEnglish
    Title of host publication2022 25th International Conference on Information Fusion, FUSION 2022
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781737749721
    DOIs
    Publication statusPublished - 2022
    Event25th International Conference on Information Fusion, FUSION 2022 - Linkoping, Sweden
    Duration: 4 Jul 20227 Jul 2022

    Publication series

    Name2022 25th International Conference on Information Fusion, FUSION 2022

    Conference

    Conference25th International Conference on Information Fusion, FUSION 2022
    Country/TerritorySweden
    CityLinkoping
    Period4/07/227/07/22

    Bibliographical note

    Funding Information:
    CR was supported by a Research Studentship jointly funded by EPSRC and the ESRC Centre for Doctoral Training on Quantification and Management of Risk and Uncertainty in Complex Systems Environments [EP/L015927/1]; SM was supported by the EPSRC through the Big Hypotheses grant [EP/R018537/1] and CR and JPG were supported by funding from the UK Health Security Agency (UKHSA).

    Publisher Copyright:
    © 2022 International Society of Information Fusion.

    Keywords

    • Differentiable particle filter
    • epidemics
    • gradients
    • NUTS
    • Particle-MCMC

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