title: Estimating a Markovian epidemic model using household serial interval data from the early phase of an epidemic.
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.
authors.affiliation: School of Mathematical Sciences, The University of Adelaide, Adelaide, Australia.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Black
authors.forename: Joshua V
authors.initials: JV
authors.lastname: Ross
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D058872:Epidemics; D005191:Family Characteristics; D006723:Hong Kong / Q000453:epidemiology; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method
references.citation: Proc Biol Sci. 2007 Feb 22;274(1609):599-604
references.pmid: 17476782
references.citation: Vaccine. 2010 Mar 11;28(12):2370-84
references.pmid: 20096762
references.citation: Math Biosci. 2010 Jan;223(1):24-31
references.pmid: 19854206
references.citation: PLoS One. 2010 Mar 18;5(3):e9666
references.pmid: 20305791
references.citation: Epidemiology. 2009 May;20(3):344-7
references.pmid: 19279492
references.citation: Proc Natl Acad Sci U S A. 2006 Apr 11;103(15):5935-40
references.pmid: 16585506
references.citation: Proc Biol Sci. 2011 Sep 22;278(1719):2753-60
references.pmid: 21288945
references.citation: Clin Infect Dis. 2011 Jan 1;52 Suppl 1:S123-30
references.pmid: 21342883
references.citation: J R Soc Interface. 2013 Feb 06;10(81):20121019
references.pmid: 23389899
references.citation: Stat Med. 2004 Nov 30;23(22):3469-87
references.pmid: 15505892
references.citation: Science. 2003 Jun 20;300(5627):1966-70
references.pmid: 12766207
references.citation: PLoS Med. 2005 Jul;2(7):e174
references.pmid: 16013892
references.citation: Proc Biol Sci. 2001 May 7;268(1470):985-93
references.pmid: 11370974
references.citation: Am J Epidemiol. 2003 Dec 1;158(11):1039-47
references.pmid: 14630599
references.citation: Epidemiology. 2012 Jul;23(4):531-42
references.pmid: 22561117
references.citation: N Engl J Med. 2009 Dec 31;361(27):2619-27
references.pmid: 20042753
references.citation: J Theor Biol. 2011 Nov 21;289:83-9
references.pmid: 21903101
references.citation: J R Soc Interface. 2008 Feb 6;5(19):171-81
references.pmid: 17638650
references.citation: Epidemiology. 2012 Jul;23(4):520-2
references.pmid: 22659545
references.citation: Nature. 2005 Sep 8;437(7056):209-14
references.pmid: 16079797
references.citation: N Engl J Med. 2009 Dec 31;361(27):2628-36
references.pmid: 20042754
references.citation: Epidemiology. 2012 Jul;23(4):510-9
references.pmid: 22441543
references.citation: Influenza Other Respir Viruses. 2011 Sep;5(5):306-16
references.pmid: 21668690
references.citation: Math Biosci. 2007 Jul;208(1):300-11
references.pmid: 17174352
references.citation: Biostatistics. 2011 Apr;12(2):303-12
references.pmid: 20858771
references.citation: Am J Epidemiol. 2008 Apr 1;167(7):775-85
references.pmid: 18230677