ArticlePloS one2013
Estimating a Markovian epidemic model using household serial interval data from the early phase of an epidemic.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Estimating the serial interval of the novel coronavirus disease (COVID-19) based on the public surveillance data in Shenzhen, China, from 19 January to 22 February 2020.Transboundary and emerging diseases · 2020Article
- Estimation in emerging epidemics: biases and remedies.Journal of the Royal Society, Interface · 2019Article
- Scabies in residential care homes: Modelling, inference and interventions for well-connected population sub-units.PLoS computational biology · 2018Article
- Analysis of influenza transmission in the households of primary and junior high school students during the 2012-13 influenza season in Odate, Japan.BMC infectious diseases · 2015Article
- The impact of prior information on estimates of disease transmissibility using Bayesian tools.PloS one · 2015Article
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Authors and funding
2 authors.
Funding
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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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