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

Content Provider	PubMed Central
Author	Simon, Cauchemez Ferguson, Neil M.
Copyright Year	2007
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 mimicks 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.
Related Links	http://dx.doi.org/10.1098/rsif.2007.1292
Ending Page	897
Page Count	13
Starting Page	885
File Format	PDF
ISSN	17425689
e-ISSN	17425662
Journal	Journal of the Royal Society Interface
Issue Number	25
Volume Number	5
Language	English
Publisher	The Royal Society
Publisher Date	2008-08-06
Access Restriction	Open
Rights Holder	The Royal Society
Subject Keyword	Biotechnology Biophysics Biochemistry Bioengineering Biomaterials Biomedical Engineering Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Biochemistry Biomaterials Biophysics Bioengineering Biomedical Engineering Biotechnology

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