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Epidemics. 2015 Mar;10:83-7. doi: 10.1016/j.epidem.2014.09.004. Epub 2014 Sep 28.
Four key challenges in infectious disease modelling using data from multiple sources.
De Angelis D1, Presanis AM2, Birrell PJ2, Tomba GS3, House T4.
Author information
Abstract
Public health-related decision-making on policies aimed at controlling epidemics is increasingly evidence-based, exploiting multiple sources of data. Policy makers rely on complex models that are required to be robust, realistically approximating epidemics and consistent with all relevant data. Meeting these requirements in a statistically rigorous and defendable manner poses a number of challenging problems. How to weight evidence from different datasets and handle dependence between them, efficiently estimate and critically assess complex models are key challenges that we expound in this paper, using examples from influenza modelling.
Copyright ? 2014 The Authors. Published by Elsevier B.V. All rights reserved.
KEYWORDS:
Bayesian; Complex models; Epidemics; Evidence synthesis; Multiple sources; Statistical inference
PMID: 25843390 [PubMed - in process] Free full text
Four key challenges in infectious disease modelling using data from multiple sources.
De Angelis D1, Presanis AM2, Birrell PJ2, Tomba GS3, House T4.
Author information
Abstract
Public health-related decision-making on policies aimed at controlling epidemics is increasingly evidence-based, exploiting multiple sources of data. Policy makers rely on complex models that are required to be robust, realistically approximating epidemics and consistent with all relevant data. Meeting these requirements in a statistically rigorous and defendable manner poses a number of challenging problems. How to weight evidence from different datasets and handle dependence between them, efficiently estimate and critically assess complex models are key challenges that we expound in this paper, using examples from influenza modelling.
Copyright ? 2014 The Authors. Published by Elsevier B.V. All rights reserved.
KEYWORDS:
Bayesian; Complex models; Epidemics; Evidence synthesis; Multiple sources; Statistical inference
PMID: 25843390 [PubMed - in process] Free full text