title: Four key challenges in infectious disease modelling using data from multiple sources.
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.
authors.affiliation: MRC Biostatistics Unit, Cambridge Institute of Public Health, Robinson Way, Cambridge CB2 0SR, UK; Public Health England, 61 Colindale Avenue, London NW9 5HT, UK. Electronic address: daniela.deangelis@mrc-bsu.cam.ac.uk.
authors.forename: Daniela
authors.initials: D
authors.lastname: De Angelis
authors.affiliation: MRC Biostatistics Unit, Cambridge Institute of Public Health, Robinson Way, Cambridge CB2 0SR, UK.
authors.forename: Anne M
authors.initials: AM
authors.lastname: Presanis
authors.affiliation: MRC Biostatistics Unit, Cambridge Institute of Public Health, Robinson Way, Cambridge CB2 0SR, UK.
authors.forename: Paul J
authors.initials: PJ
authors.lastname: Birrell
authors.affiliation: Department of Mathematics, University of Rome Tor Vergata, Rome, Italy.
authors.forename: Gianpaolo Scalia
authors.initials: GS
authors.lastname: Tomba
authors.affiliation: Warwick Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK.
authors.forename: Thomas
authors.initials: T
authors.lastname: House
mesh_terms: D003141:Communicable Diseases / Q000453:epidemiology*; D003625:Data Collection; D058872:Epidemics / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D013223:Statistics as Topic
keywords: Bayesian; Complex models; Epidemics; Evidence synthesis; Multiple sources; Statistical inference
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