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

Daniela De Angelis; Anne M. Presanis; Paul J. Birrell; Gianpaolo Scalia Tomba; Thomas House

doi:10.1016/j.epidem.2014.09.004

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

Daniela De Angelis^*
, Anne M. Presanis
, Paul J. Birrell
, Gianpaolo Scalia Tomba
, Thomas House

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

62 Citations (Scopus)

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.

Original language	English
Pages (from-to)	83-87
Number of pages	5
Journal	Epidemics
Volume	10
DOIs	https://doi.org/10.1016/j.epidem.2014.09.004
Publication status	Published - 1 Mar 2015

Bibliographical note

Publisher Copyright:
© 2014 The Authors.

Keywords

Bayesian
Complex models
Epidemics
Evidence synthesis
Multiple sources
Statistical inference

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.epidem.2014.09.004

Cite this

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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.",

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doi = "10.1016/j.epidem.2014.09.004",

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AU - Presanis, Anne M.

AU - Birrell, Paul J.

AU - Tomba, Gianpaolo Scalia

AU - House, Thomas

PY - 2015/3/1

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AB - 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.

KW - Bayesian

KW - Complex models

KW - Epidemics

KW - Evidence synthesis

KW - Multiple sources

KW - Statistical inference

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M3 - Article

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SN - 1755-4365

VL - 10

SP - 83

EP - 87

JO - Epidemics

JF - Epidemics

ER -

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

Abstract

Bibliographical note

Keywords

UN SDGs

Access to Document

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