Four key challenges in infectious disease modelling using data from multiple sources.
Published version
Peer-reviewed
Repository URI
Repository DOI
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
1878-0067
Volume Title
10
Publisher
Elsevier BV