Literature DB >> 23440509

Who was the infector--probabilities in the presence of variability in latent and infectious times.

Åke Svensson1.   

Abstract

The probability that an observed infection has been transmitted from a particular member of a set of potential infectors is calculated. The calculations only use knowledge of the times of infection. It is shown that the probabilities depend on individual variability in latent and infectious times. The analysis are based on different background information and different assumptions on the progress of infectivity. The results are illustrated by numerical calculations and simulations.

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Year:  2013        PMID: 23440509     DOI: 10.1007/s00285-013-0658-6

Source DB:  PubMed          Journal:  J Math Biol        ISSN: 0303-6812            Impact factor:   2.259


  9 in total

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2.  A note on generation times in epidemic models.

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4.  Some model based considerations on observing generation times for communicable diseases.

Authors:  Gianpaolo Scalia Tomba; Ake Svensson; Tommi Asikainen; Johan Giesecke
Journal:  Math Biosci       Date:  2009-10-23       Impact factor: 2.144

5.  Time lines of infection and disease in human influenza: a review of volunteer challenge studies.

Authors:  Fabrice Carrat; Elisabeta Vergu; Neil M Ferguson; Magali Lemaitre; Simon Cauchemez; Steve Leach; Alain-Jacques Valleron
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6.  Robust reconstruction and analysis of outbreak data: influenza A(H1N1)v transmission in a school-based population.

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Journal:  Am J Epidemiol       Date:  2012-07-12       Impact factor: 4.897

7.  Reconstructing disease outbreaks from genetic data: a graph approach.

Authors:  T Jombart; R M Eggo; P J Dodd; F Balloux
Journal:  Heredity (Edinb)       Date:  2010-06-16       Impact factor: 3.821

8.  Methods to infer transmission risk factors in complex outbreak data.

Authors:  Simon Cauchemez; Neil M Ferguson
Journal:  J R Soc Interface       Date:  2011-08-10       Impact factor: 4.118

9.  Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures.

Authors:  Jacco Wallinga; Peter Teunis
Journal:  Am J Epidemiol       Date:  2004-09-15       Impact factor: 4.897

  9 in total

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