Literature DB >> 21071607

Contact intervals, survival analysis of epidemic data, and estimation of R(0).

Eben Kenah1.   

Abstract

We argue that the time from the onset of infectiousness to infectious contact, which we call the "contact interval," is a better basis for inference in epidemic data than the generation or serial interval. Since contact intervals can be right censored, survival analysis is the natural approach to estimation. Estimates of the contact interval distribution can be used to estimate R(0) in both mass-action and network-based models. We apply these methods to 2 data sets from the 2009 influenza A(H1N1) pandemic.

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Year:  2010        PMID: 21071607      PMCID: PMC3114649          DOI: 10.1093/biostatistics/kxq068

Source DB:  PubMed          Journal:  Biostatistics        ISSN: 1465-4644            Impact factor:   5.899


  17 in total

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2.  Estimating in real time the efficacy of measures to control emerging communicable diseases.

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3.  A note on generation times in epidemic models.

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4.  Early transmission characteristics of influenza A(H1N1)v in Australia: Victorian state, 16 May - 3 June 2009.

Authors:  Es McBryde; I Bergeri; C van Gemert; J Rotty; Ej Headley; K Simpson; Ra Lester; M Hellard; Je Fielding
Journal:  Euro Surveill       Date:  2009-10-22

5.  The transmissibility and control of pandemic influenza A (H1N1) virus.

Authors:  Yang Yang; Jonathan D Sugimoto; M Elizabeth Halloran; Nicole E Basta; Dennis L Chao; Laura Matrajt; Gail Potter; Eben Kenah; Ira M Longini
Journal:  Science       Date:  2009-09-10       Impact factor: 47.728

6.  A likelihood-based method for real-time estimation of the serial interval and reproductive number of an epidemic.

Authors:  L Forsberg White; M Pagano
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7.  Transmissibility of 1918 pandemic influenza.

Authors:  Christina E Mills; James M Robins; Marc Lipsitch
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8.  Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures.

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Authors:  Neil M Ferguson; Derek A T Cummings; Christophe Fraser; James C Cajka; Philip C Cooley; Donald S Burke
Journal:  Nature       Date:  2006-04-26       Impact factor: 49.962

10.  Pandemic potential of a strain of influenza A (H1N1): early findings.

Authors:  Christophe Fraser; Christl A Donnelly; Simon Cauchemez; William P Hanage; Maria D Van Kerkhove; T Déirdre Hollingsworth; Jamie Griffin; Rebecca F Baggaley; Helen E Jenkins; Emily J Lyons; Thibaut Jombart; Wes R Hinsley; Nicholas C Grassly; Francois Balloux; Azra C Ghani; Neil M Ferguson; Andrew Rambaut; Oliver G Pybus; Hugo Lopez-Gatell; Celia M Alpuche-Aranda; Ietza Bojorquez Chapela; Ethel Palacios Zavala; Dulce Ma Espejo Guevara; Francesco Checchi; Erika Garcia; Stephane Hugonnet; Cathy Roth
Journal:  Science       Date:  2009-05-11       Impact factor: 47.728

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  15 in total

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

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Journal:  J Math Biol       Date:  2013-02-26       Impact factor: 2.259

3.  Semiparametric Relative-risk Regression for Infectious Disease Transmission Data.

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4.  A hybrid EM and Monte Carlo EM algorithm and its application to analysis of transmission of infectious diseases.

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Journal:  Biometrics       Date:  2012-04-16       Impact factor: 2.571

5.  Nonparametric survival analysis of infectious disease data.

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Journal:  J R Stat Soc Series B Stat Methodol       Date:  2013-03       Impact factor: 4.488

6.  Inference of R(0) and transmission heterogeneity from the size distribution of stuttering chains.

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7.  Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data.

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Review 8.  Molecular evolution and emergence of avian gammacoronaviruses.

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9.  Key questions for modelling COVID-19 exit strategies.

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10.  Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees.

Authors:  Eben Kenah; Tom Britton; M Elizabeth Halloran; Ira M Longini
Journal:  PLoS Comput Biol       Date:  2016-04-12       Impact factor: 4.779

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