Literature DB >> 31975746

Evidence Synthesis for Stochastic Epidemic Models.

Paul J Birrell1, Daniela De Angelis2, Anne M Presanis3.   

Abstract

In recent years, the role of epidemic models in informing public health policies has progressively grown. Models have become increasingly realistic and more complex, requiring the use of multiple data sources to estimate all quantities of interest. This review summarises the different types of stochastic epidemic models that use evidence synthesis and highlights current challenges.

Entities:  

Keywords:  Evidence synthesis; epidemic modelling; mechanistic modelling; state-space models

Year:  2018        PMID： 31975746      PMCID： PMC6978147          DOI： 10.1214/17-STS631

Source DB:  PubMed          Journal:  Stat Sci        ISSN： 0883-4237            Impact factor:   2.901


  26 in total

1.  Capturing the time-varying drivers of an epidemic using stochastic dynamical systems.

Authors:  Joseph Dureau; Konstantinos Kalogeropoulos; Marc Baguelin
Journal:  Biostatistics       Date:  2013-01-04       Impact factor: 5.899

2.  Joining and splitting models with Markov melding.

Authors:  Robert J B Goudie; Anne M Presanis; David Lunn; Daniela De Angelis; Lorenz Wernisch
Journal:  Bayesian Anal       Date:  2019-01       Impact factor: 3.728

3.  Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London.

Authors:  Paul J Birrell; Georgios Ketsetzis; Nigel J Gay; Ben S Cooper; Anne M Presanis; Ross J Harris; André Charlett; Xu-Sheng Zhang; Peter J White; Richard G Pebody; Daniela De Angelis
Journal:  Proc Natl Acad Sci U S A       Date:  2011-10-31       Impact factor: 11.205

4.  Increased transmissibility explains the third wave of infection by the 2009 H1N1 pandemic virus in England.

Authors:  Ilaria Dorigatti; Simon Cauchemez; Neil M Ferguson
Journal:  Proc Natl Acad Sci U S A       Date:  2013-07-23       Impact factor: 11.205

5.  Inference for nonlinear epidemiological models using genealogies and time series.

Authors:  David A Rasmussen; Oliver Ratmann; Katia Koelle
Journal:  PLoS Comput Biol       Date:  2011-08-25       Impact factor: 4.475

6.  Estimating the number of people with hepatitis C virus who have ever injected drugs and have yet to be diagnosed: an evidence synthesis approach for Scotland.

Authors:  Teresa C Prevost; Anne M Presanis; Avril Taylor; David J Goldberg; Sharon J Hutchinson; Daniela De Angelis
Journal:  Addiction       Date:  2015-06-08       Impact factor: 6.526

7.  Reconstructing a spatially heterogeneous epidemic: Characterising the geographic spread of 2009 A/H1N1pdm infection in England.

Authors:  Paul J Birrell; Xu-Sheng Zhang; Richard G Pebody; Nigel J Gay; Daniela De Angelis
Journal:  Sci Rep       Date:  2016-07-11       Impact factor: 4.379

8.  Reconstructing transmission trees for communicable diseases using densely sampled genetic data.

Authors:  Colin J Worby; Philip D O'Neill; Theodore Kypraios; Julie V Robotham; Daniela De Angelis; Edward J P Cartwright; Sharon J Peacock; Ben S Cooper
Journal:  Ann Appl Stat       Date:  2016-03-25       Impact factor: 2.083

9.  Phylodynamic inference and model assessment with approximate bayesian computation: influenza as a case study.

Authors:  Oliver Ratmann; Gé Donker; Adam Meijer; Christophe Fraser; Katia Koelle
Journal:  PLoS Comput Biol       Date:  2012-12-27       Impact factor: 4.475

10.  Bayesian evidence synthesis to estimate HIV prevalence in men who have sex with men in Poland at the end of 2009.

Authors:  M Rosinska; P Gwiazda; D De Angelis; A M Presanis
Journal:  Epidemiol Infect       Date:  2015-11-06       Impact factor: 2.451
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  4 in total

Review 1.  Outbreak analytics: a developing data science for informing the response to emerging pathogens.

Authors:  Jonathan A Polonsky; Amrish Baidjoe; Zhian N Kamvar; Anne Cori; Kara Durski; W John Edmunds; Rosalind M Eggo; Sebastian Funk; Laurent Kaiser; Patrick Keating; Olivier le Polain de Waroux; Michael Marks; Paula Moraga; Oliver Morgan; Pierre Nouvellet; Ruwan Ratnayake; Chrissy H Roberts; Jimmy Whitworth; Thibaut Jombart
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2019-07-08       Impact factor: 6.237

2.  Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling.

Authors:  Adam Spannaus; Theodore Papamarkou; Samantha Erwin; J Blair Christian
Journal:  Sci Rep       Date:  2022-06-24       Impact factor: 4.996

3.  Early dynamics of transmission and control of COVID-19: a mathematical modelling study.

Authors:  Adam J Kucharski; Timothy W Russell; Charlie Diamond; Yang Liu; John Edmunds; Sebastian Funk; Rosalind M Eggo
Journal:  Lancet Infect Dis       Date:  2020-03-11       Impact factor: 25.071

4.  Mathematical modelling on diffusion and control of COVID-19.

Authors:  M Veera Krishna
Journal:  Infect Dis Model       Date:  2020-08-21
  4 in total

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