Linked Life Data

18174112

Source:http://linkedlifedata.com/resource/pubmed/id/18174112

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
25
pubmed:dateCreated
2008-6-23
pubmed:abstractText
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.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1742-5689
pubmed:author
pubmed:issnType
Print
pubmed:day
6
pubmed:volume
5
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
885-97
pubmed:dateRevised
2009-11-18
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Likelihood-based estimation of continuous-time epidemic models from time-series data: application to measles transmission in London.
pubmed:affiliation
MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, Norfolk Place, London W2 1PG, UK. s.cauchemez@imperial.ac.uk
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural