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Type: Journal article
Title: Inference of epidemiological parameters from household stratified data
Author: Walker, J.
Ross, J.
Black, A.
Citation: PLoS ONE, 2017; 12(10):e0185910-1-e0185910-21
Publisher: Public Library Science
Issue Date: 2017
ISSN: 1932-6203
Statement of
James N. Walker, Joshua V. Ross, Andrew J. Black
Abstract: We consider a continuous-time Markov chain model of SIR disease dynamics with two levels of mixing. For this so-called stochastic households model, we provide two methods for inferring the model parameters-governing within-household transmission, recovery, and between-household transmission-from data of the day upon which each individual became infectious and the household in which each infection occurred, as might be available from First Few Hundred studies. Each method is a form of Bayesian Markov Chain Monte Carlo that allows us to calculate a joint posterior distribution for all parameters and hence the household reproduction number and the early growth rate of the epidemic. The first method performs exact Bayesian inference using a standard data-augmentation approach; the second performs approximate Bayesian inference based on a likelihood approximation derived from branching processes. These methods are compared for computational efficiency and posteriors from each are compared. The branching process is shown to be a good approximation and remains computationally efficient as the amount of data is increased.
Keywords: Convolution; algorithms; approximation methods; Markov models; random variables; disease dynamics; epidemiology; simulation and modelling
Rights: © 2017 Walker et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
RMID: 0030076913
DOI: 10.1371/journal.pone.0185910
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Appears in Collections:Mathematical Sciences publications

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