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

被引：46

作者：

De Angelis, Daniela ^{[1
,2
]}

Presanis, Anne M. ^{[1
]}

Birrell, Paul J. ^{[1
]}

Tomba, Gianpaolo Scalia ^{[3
]}

House, Thomas ^{[4
]}

机构：

[1] Cambridge Inst Publ Hlth, MRC, Biostat Unit, Cambridge CB2 OSR, England

[2] Publ Hlth England, London NW9 5HT, England

[3] Univ Roma Tor Vergata, Dept Math, Rome, Italy

[4] Univ Warwick, Warwick Math Inst, Coventry CV4 7AL, W Midlands, England

来源：

EPIDEMICS | 2015年 / 10卷

基金：

英国医学研究理事会; 英国工程与自然科学研究理事会;

关键词：

Evidence synthesis; Bayesian; Statistical inference; Multiple sources; Epidemics; Complex models; BAYESIAN COMPUTATION; A/H1N1; INFLUENZA; EPIDEMIC MODELS; MONTE-CARLO; DYNAMICS; ENGLAND; H1N1; INFERENCE; SEVERITY; HIV;

D O I：

10.1016/j.epidem.2014.09.004

中图分类号：

R51 [传染病];

学科分类号：

100401 ;

摘要：

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. (C) 2014 The Authors. Published by Elsevier B.V.

引用

页码：83 / 87

页数：5

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