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Estimating a Markovian Epidemic Model Using Household Serial Interval Data from the Early Phase of an Epidemic

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  • 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.

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  • Andrew J Black & Joshua V Ross, 2013. "Estimating a Markovian Epidemic Model Using Household Serial Interval Data from the Early Phase of an Epidemic," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    • Handle: RePEc:plo:pone00:0073420
    DOI: 10.1371/journal.pone.0073420
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    1. Neil M. Ferguson & Derek A.T. Cummings & Simon Cauchemez & Christophe Fraser & Steven Riley & Aronrag Meeyai & Sopon Iamsirithaworn & Donald S. Burke, 2005. "Strategies for containing an emerging influenza pandemic in Southeast Asia," Nature, Nature, vol. 437(7056), pages 209-214, September.
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    Cited by:

    1. Timothy Kinyanjui & Jo Middleton & Stefan Güttel & Jackie Cassell & Joshua Ross & Thomas House, 2018. "Scabies in residential care homes: Modelling, inference and interventions for well-connected population sub-units," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-24, March.

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