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Four key challenges in infectious disease modelling using data from multiple sources
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Angelis, Daniela De, Presanis, Anne M., Birrell, Paul J., Tomba, Gianpaolo Scalia and House, Thomas A. (2015) Four key challenges in infectious disease modelling using data from multiple sources. Epidemics, 10 . pp. 83-87. doi:10.1016/j.epidem.2014.09.004 ISSN 1755-4365.
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Official URL: http://dx.doi.org/10.1016/j.epidem.2014.09.004
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.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics | ||||||||
Journal or Publication Title: | Epidemics | ||||||||
Publisher: | Elsevier BV | ||||||||
ISSN: | 1755-4365 | ||||||||
Official Date: | March 2015 | ||||||||
Dates: |
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Volume: | 10 | ||||||||
Page Range: | pp. 83-87 | ||||||||
DOI: | 10.1016/j.epidem.2014.09.004 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) |
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