Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling
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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.
Swallow , B , Birrell , P , Blake , J , Burgman , M , Challenor , P , Coffeng , L E , Dawid , P , De Angelis , D , Goldstein , M , Hemming , V , Marion , G , McKinley , T J , Overton , C E , Panovska-Griffiths , J , Pellis , L , Probert , W , Shea , K , Villela , D & Vernon , I 2022 , ' Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling ' , Epidemics , vol. 38 , 100547 . https://doi.org/10.1016/j.epidem.2022.100547
Copyright © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
DescriptionDDA, JB and PB are funded by MRC (Unit Programme number MC/UU/00002/11); DDA is also supported by the NIHR Health Protection Unit in Behavioural Science and Evaluation. JPG's work was supported by funding from the UK Health Security Agency and the UK Department of Health and Social Care.
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