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

Published version
Peer-reviewed

Type

Article

Change log

Authors

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

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.

Description

Keywords

Bayesian, Complex models, Epidemics, Evidence synthesis, Multiple sources, Statistical inference, Communicable Diseases, Data Collection, Epidemics, Humans, Models, Statistical, Statistics as Topic

Journal Title

Epidemics

Conference Name

Journal ISSN

1755-4365
1878-0067

Volume Title

10

Publisher

Elsevier BV