Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling

Swallow, Ben; Birrell, Paul; Blake, Joshua; Burgman, Mark; Challenor, Peter; Coffeng, Luc E.; Dawid, Philip; De Angelis, Daniela; 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

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.

Citation

Swallow, B., Birrell, P., Blake, J., Burgman, M., Challenor, P., Coffeng, L. E., …Vernon, I. (2022). Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling. Epidemics, 38, https://doi.org/10.1016/j.epidem.2022.100547

• Journal Article Type: Article
• Acceptance Date: Feb 9, 2022
• Online Publication Date: Feb 15, 2022
• Publication Date: 2022-03
• Deposit Date: Apr 5, 2022
• Publicly Available Date: Apr 6, 2022
• Journal: Epidemics
• Print ISSN: 1755-4365
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 38
• DOI: https://doi.org/10.1016/j.epidem.2022.100547
• Public URL: https://durham-repository.worktribe.com/output/1208586