Four key challenges in infectious disease modelling using data from multiple sources

Four key challenges in infectious disease modelling using data from multiple sources

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. Mee...

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Bibliographic Details
Main Authors: Daniela De Angelis, Anne M. Presanis, Paul J. Birrell, Gianpaolo Scalia Tomba, Thomas House
Format: Article
Language:English
Published: Elsevier 2015-03-01
Series:Epidemics
Subjects:
Evidence synthesis
Bayesian
Statistical inference
Multiple sources
Epidemics
Complex models
Online Access:http://www.sciencedirect.com/science/article/pii/S175543651400053X
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spelling doaj-8e8bee62a27b4f16833d91083ca36ae02020-11-24T22:27:27ZengElsevierEpidemics1755-43651878-00672015-03-0110C838710.1016/j.epidem.2014.09.004Four key challenges in infectious disease modelling using data from multiple sourcesDaniela De Angelis0Anne M. Presanis1Paul J. Birrell2Gianpaolo Scalia Tomba3Thomas House4MRC Biostatistics Unit, Cambridge Institute of Public Health, Robinson Way, Cambridge CB2 0SR, UKMRC Biostatistics Unit, Cambridge Institute of Public Health, Robinson Way, Cambridge CB2 0SR, UKMRC Biostatistics Unit, Cambridge Institute of Public Health, Robinson Way, Cambridge CB2 0SR, UKDepartment of Mathematics, University of Rome Tor Vergata, Rome, ItalyWarwick Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK 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. http://www.sciencedirect.com/science/article/pii/S175543651400053XEvidence synthesisBayesianStatistical inferenceMultiple sourcesEpidemicsComplex models
collection DOAJ
language English
format Article
sources DOAJ
author Daniela De Angelis
Anne M. Presanis
Paul J. Birrell
Gianpaolo Scalia Tomba
Thomas House
spellingShingle Daniela De Angelis
Anne M. Presanis
Paul J. Birrell
Gianpaolo Scalia Tomba
Thomas House
Four key challenges in infectious disease modelling using data from multiple sources
Epidemics
Evidence synthesis
Bayesian
Statistical inference
Multiple sources
Epidemics
Complex models
author_facet Daniela De Angelis
Anne M. Presanis
Paul J. Birrell
Gianpaolo Scalia Tomba
Thomas House
author_sort Daniela De Angelis
title Four key challenges in infectious disease modelling using data from multiple sources
title_short Four key challenges in infectious disease modelling using data from multiple sources
title_full Four key challenges in infectious disease modelling using data from multiple sources
title_fullStr Four key challenges in infectious disease modelling using data from multiple sources
title_full_unstemmed Four key challenges in infectious disease modelling using data from multiple sources
title_sort four key challenges in infectious disease modelling using data from multiple sources
publisher Elsevier
series Epidemics
issn 1755-4365
1878-0067
publishDate 2015-03-01
description 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.
topic Evidence synthesis
Bayesian
Statistical inference
Multiple sources
Epidemics
Complex models
url http://www.sciencedirect.com/science/article/pii/S175543651400053X
work_keys_str_mv AT danieladeangelis fourkeychallengesininfectiousdiseasemodellingusingdatafrommultiplesources
AT annempresanis fourkeychallengesininfectiousdiseasemodellingusingdatafrommultiplesources
AT pauljbirrell fourkeychallengesininfectiousdiseasemodellingusingdatafrommultiplesources
AT gianpaoloscaliatomba fourkeychallengesininfectiousdiseasemodellingusingdatafrommultiplesources
AT thomashouse fourkeychallengesininfectiousdiseasemodellingusingdatafrommultiplesources
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