Likelihood-based estimation of continuous-time epidemic models from time-series data: application to measles transmission in London.

TitleLikelihood-based estimation of continuous-time epidemic models from time-series data: application to measles transmission in London.
Publication TypeJournal Article
Year of Publication2008
AuthorsCauchemez S, Ferguson NM
JournalJ R Soc Interface
Volume5
Issue25
Pagination885-97
Date Published2008 Aug 6
ISSN1742-5689
KeywordsBayes Theorem, Computer Simulation, Data Interpretation, Statistical, Disease Outbreaks, Humans, Likelihood Functions, London, Measles, Models, Biological, Time Factors
Abstract

We present a new statistical approach to analyse epidemic time-series data. A major difficulty for inference is that (i) the latent transmission process is partially observed and (ii) observed quantities are further aggregated temporally. We develop a data augmentation strategy to tackle these problems and introduce a diffusion process that mimics the susceptible-infectious-removed (SIR) epidemic process, but that is more tractable analytically. While methods based on discrete-time models require epidemic and data collection processes to have similar time scales, our approach, based on a continuous-time model, is free of such constraint. Using simulated data, we found that all parameters of the SIR model, including the generation time, were estimated accurately if the observation interval was less than 2.5 times the generation time of the disease. Previous discrete-time TSIR models have been unable to estimate generation times, given that they assume the generation time is equal to the observation interval. However, we were unable to estimate the generation time of measles accurately from historical data. This indicates that simple models assuming homogenous mixing (even with age structure) of the type which are standard in mathematical epidemiology miss key features of epidemics in large populations.

DOI10.1098/rsif.2007.1292
Alternate JournalJ R Soc Interface
PubMed ID18174112
PubMed Central IDPMC2607466
Grant ListU54 GM088491 / GM / NIGMS NIH HHS / United States
/ / Medical Research Council / United Kingdom
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