Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling.
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Authors
Swallow, Ben
Birrell, Paul
Blake, Joshua
Burgman, Mark
Challenor, Peter
Coffeng, Luc E
Dawid, Philip
Goldstein, Michael
Hemming, Victoria
Marion, Glenn
McKinley, Trevelyan J
Overton, Christopher E
Panovska-Griffiths, Jasmina
Pellis, Lorenzo
Probert, Will
Shea, Katriona
Villela, Daniel
Vernon, Ian
Publication Date
2022-03Journal Title
Epidemics
ISSN
1755-4365
Publisher
Elsevier BV
Volume
38
Number
100547
Pages
100547
Type
Article
This Version
VoR
Physical Medium
Print-Electronic
Metadata
Show full item recordCitation
Swallow, B., Birrell, P., Blake, J., Burgman, M., Challenor, P., Coffeng, L. E., Dawid, P., et al. (2022). Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling.. Epidemics, 38 (100547), 100547. https://doi.org/10.1016/j.epidem.2022.100547
Abstract
The estimation of parameters and model structure for informing infectious disease response has become a focal point of the recent pandemic. However, it has also highlighted a plethora of challenges remaining in the fast and robust extraction of information using data and models to help inform policy. In this paper, we identify and discuss four broad challenges in the estimation paradigm relating to infectious disease modelling, namely the Uncertainty Quantification framework, data challenges in estimation, model-based inference and prediction, and expert judgement. We also postulate priorities in estimation methodology to facilitate preparation for future pandemics.
Keywords
Expert elicitation, Pandemic modelling, Statistical estimation, Uncertainty quantification, Forecasting, Pandemics, Uncertainty
Sponsorship
Engineering and Physical Sciences Research Council (EP/R014604/1)
MRC (via University of Warwick) (MR/V038613/1)
Identifiers
External DOI: https://doi.org/10.1016/j.epidem.2022.100547
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336593
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Licence URL: https://creativecommons.org/licenses/by-nc-nd/4.0/
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