Estimating a Markovian Epidemic Model Using Household Serial Interval Data from the Early Phase of an Epidemic
Open Access
- 30 August 2013
- journal article
- research article
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 8 (8), e73420
- https://doi.org/10.1371/journal.pone.0073420
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
This publication has 30 references indexed in Scilit:
- Epidemiological consequences of household-based antiviral prophylaxis for pandemic influenzaJournal of The Royal Society Interface, 2013
- Estimating Rates of Carriage Acquisition and Clearance and Competitive Ability for Pneumococcal Serotypes in Kenya With a Markov Transition ModelEpidemiology, 2012
- Modelling the impact of local reactive school closures on critical care provision during an influenza pandemicProceedings Of The Royal Society B-Biological Sciences, 2011
- Serial Intervals and the Temporal Distribution of Secondary Infections within Households of 2009 Pandemic Influenza A (H1N1): Implications for Influenza Control RecommendationsClinical Infectious Diseases, 2010
- Vaccination against pandemic influenza A/H1N1v in England: A real-time economic evaluationVaccine, 2010
- Estimation of the Serial Interval of InfluenzaEpidemiology, 2009
- Time Lines of Infection and Disease in Human Influenza: A Review of Volunteer Challenge StudiesAmerican Journal of Epidemiology, 2008
- On methods for studying stochastic disease dynamicsJournal of The Royal Society Interface, 2007
- Mitigation strategies for pandemic influenza in the United StatesProceedings of the National Academy of Sciences, 2006
- A Bayesian MCMC approach to study transmission of influenza: application to household longitudinal dataStatistics in Medicine, 2004