Linked Life Data

10704637

Source:http://linkedlifedata.com/resource/pubmed/id/10704637

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2000-4-25
pubmed:abstractText
We consider a simple unstructured individual based stochastic epidemic model with contact tracing. Even in the onset of the epidemic, contact tracing implies that infected individuals do not act independent of each other. Nevertheless, it is possible to analyze the embedded non-stationary Galton-Watson process. Based upon this analysis, threshold theorems and also the probability for major outbreaks can be derived. Furthermore, it is possible to obtain a deterministic model that approximates the stochastic process, and in this way, to determine the prevalence of disease in the quasi-stationary state and to investigate the dynamics of the epidemic.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
0025-5564
pubmed:author
pubmed:issnType
Print
pubmed:volume
164
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
39-64
pubmed:dateRevised
2009-11-11
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
Contact tracing in stochastic and deterministic epidemic models.
pubmed:affiliation
Biomathematik, Universität Tübingen, Auf der Morgenstelle 10, D-72076, Tübingen, Germany. johannes.mueller@uni-tuebingen.de
pubmed:publicationType
Journal Article, Comparative Study