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

Epidemics. 2015 Mar:10:83-7. doi: 10.1016/j.epidem.2014.09.004. Epub 2014 Sep 28.

Authors

Daniela De Angelis¹, Anne M Presanis², Paul J Birrell², Gianpaolo Scalia Tomba³, Thomas House⁴

Affiliations

¹ 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: [email protected].
² MRC Biostatistics Unit, Cambridge Institute of Public Health, Robinson Way, Cambridge CB2 0SR, UK.
³ Department of Mathematics, University of Rome Tor Vergata, Rome, Italy.
⁴ Warwick Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK.

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.

Keywords: Bayesian; Complex models; Epidemics; Evidence synthesis; Multiple sources; Statistical inference.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Communicable Diseases / epidemiology*
Data Collection
Epidemics / statistics & numerical data
Humans
Models, Statistical
Statistics as Topic

Abstract

Publication types

MeSH terms

Grants and funding