Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/81026
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Type: Journal article
Title: Estimating a Markovian epidemic model using household serial interval data from the early phase of an epidemic
Author: Black, A.
Ross, J.
Citation: PLoS One, 2013; 8(8):1-8
Publisher: Public Library of Science
Issue Date: 2013
ISSN: 1932-6203
1932-6203
Statement of
Responsibility: 
Andrew J. Black, Joshua V. Ross
Abstract: The clinical serial interval of an infectious disease is the time between date of symptom onset in an index case and the date of symptom onset in one of its secondary cases. It is a quantity which is commonly collected during a pandemic and is of fundamental importance to public health policy and mathematical modelling. In this paper we present a novel method for calculating the serial interval distribution for a Markovian model of household transmission dynamics. This allows the use of Bayesian MCMC methods, with explicit evaluation of the likelihood, to fit to serial interval data and infer parameters of the underlying model. We use simulated and real data to verify the accuracy of our methodology and illustrate the importance of accounting for household size. The output of our approach can be used to produce posterior distributions of population level epidemic characteristics.
Keywords: Humans; Monte Carlo Method; Bayes Theorem; Markov Chains; Family Characteristics; Models, Biological; Computer Simulation; Hong Kong; Influenza, Human; Epidemics
Rights: © 2013 Black, Ross. 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: 0020131367
DOI: 10.1371/journal.pone.0073420
Appears in Collections:Mathematical Sciences publications

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