Infectious disease transmission as a forensic problem: who infected whom?
Identifieur interne : 001A23 ( Main/Exploration ); précédent : 001A22; suivant : 001A24Infectious disease transmission as a forensic problem: who infected whom?
Auteurs : Peter Teunis [Pays-Bas, États-Unis] ; Janneke C. M. Heijne [Suisse] ; Faizel Sukhrie [Pays-Bas] ; Jan Van Eijkeren [Pays-Bas] ; Marion Koopmans [Pays-Bas] ; Mirjam Kretzschmar [Pays-Bas]Source :
- Journal of the Royal Society Interface [ 1742-5689 ] ; 2013.
Abstract
Observations on infectious diseases often consist of a sample of cases, distinguished by symptoms, and other characteristics, such as onset dates, spatial locations, genetic sequence of the pathogen and/or physiological and clinical data. Cases are often clustered, in space and time, suggesting that they are connected. By defining kernel functions for pairwise analysis of cases, a matrix of transmission probabilities can be estimated. We set up a Bayesian framework to integrate various sources of information to estimate the transmission network. The method is illustrated by analysing data from a multi-year study (2002–2007) of nosocomial outbreaks of norovirus in a large university hospital in the Netherlands. The study included 264 cases, the norovirus genotype was known in approximately 60 per cent of the patients. Combining all the available data allowed likely identification of individual transmission links between most of the cases (72%). This illustrates that the proposed method can be used to accurately reconstruct transmission networks, enhancing our understanding of outbreak dynamics and possibly leading to new insights into how to prevent outbreaks.
Url:
DOI: 10.1098/rsif.2012.0955
Affiliations:
- Pays-Bas, Suisse, États-Unis
- Canton de Berne, Géorgie (États-Unis), Hollande-Méridionale, Utrecht (province)
- Berne, Rotterdam, Utrecht
- Université de Berne
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R. Soc. Interface</title><idno type="ISSN">1742-5689</idno><idno type="eISSN">1742-5662</idno><imprint><publisher>The Royal Society</publisher><date type="published">2013</date><biblScope unit="vol">10</biblScope><biblScope unit="issue">81</biblScope></imprint><idno type="ISSN">1742-5689</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1742-5689</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract">Observations on infectious diseases often consist of a sample of cases, distinguished by symptoms, and other characteristics, such as onset dates, spatial locations, genetic sequence of the pathogen and/or physiological and clinical data. Cases are often clustered, in space and time, suggesting that they are connected. By defining kernel functions for pairwise analysis of cases, a matrix of transmission probabilities can be estimated. We set up a Bayesian framework to integrate various sources of information to estimate the transmission network. The method is illustrated by analysing data from a multi-year study (2002–2007) of nosocomial outbreaks of norovirus in a large university hospital in the Netherlands. The study included 264 cases, the norovirus genotype was known in approximately 60 per cent of the patients. Combining all the available data allowed likely identification of individual transmission links between most of the cases (72%). 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M." last="Heijne">Janneke C. M. Heijne</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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