Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling

Ben Swallow; Paul Birrell; Joshua Blake; Mark Burgman; Peter Challenor; Luc E Coffeng; Philip Dawid; Daniela De Angelis; Michael Goldstein; Victoria Hemming; Glenn Marion; Trevelyan J McKinley; Christopher E Overton; Jasmina Panovska-Griffiths; Lorenzo Pellis; Will Probert; Katriona Shea; Daniel Villela; Ian Vernon

doi:10.1016/j.epidem.2022.100547

Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling

Ben Swallow^*
, Paul Birrell
, Joshua Blake
, Mark Burgman
, Peter Challenor
, Luc E Coffeng
, Philip Dawid
, Daniela De Angelis
, Michael Goldstein
, Victoria Hemming
, Glenn Marion
, Trevelyan J McKinley
, Christopher E Overton
, Jasmina Panovska-Griffiths
, Lorenzo Pellis
, Will Probert
, Katriona Shea
, Daniel Villela
, Ian Vernon

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	100547
Number of pages	12
Journal	Epidemics
Volume	38
Early online date	15 Feb 2022
DOIs	https://doi.org/10.1016/j.epidem.2022.100547
Publication status	Published - Mar 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Forecasting
Pandemics
Uncertainty

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10.1016/j.epidem.2022.100547Licence: CC BY-NC-ND

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Copyright © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
Final published version, 612 KBLicence: CC BY-NC-ND

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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, 38, Article 100547. https://doi.org/10.1016/j.epidem.2022.100547

@article{13fbc35c7eac473fae87fd5606577022,

title = "Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling",

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 = "Forecasting, Pandemics, Uncertainty",

author = "Ben Swallow and Paul Birrell and Joshua Blake and Mark Burgman and Peter Challenor and Coffeng, \{Luc E\} and Philip Dawid and \{De Angelis\}, Daniela and Michael Goldstein and Victoria Hemming and Glenn Marion and McKinley, \{Trevelyan J\} and Overton, \{Christopher E\} and Jasmina Panovska-Griffiths and Lorenzo Pellis and Will Probert and Katriona Shea and Daniel Villela and Ian Vernon",

note = "DDA, 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.",

year = "2022",

month = mar,

doi = "10.1016/j.epidem.2022.100547",

language = "English",

volume = "38",

journal = "Epidemics",

issn = "1878-0067",

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Swallow, B, Birrell, P, Blake, J, Burgman, M, Challenor, P, Coffeng, LE, Dawid, P, De Angelis, D, Goldstein, M, Hemming, V, Marion, G, McKinley, TJ, Overton, CE, 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

TY - JOUR

T1 - Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling

AU - Swallow, Ben

AU - Birrell, Paul

AU - Blake, Joshua

AU - Burgman, Mark

AU - Challenor, Peter

AU - Coffeng, Luc E

AU - Dawid, Philip

AU - De Angelis, Daniela

AU - Goldstein, Michael

AU - Hemming, Victoria

AU - Marion, Glenn

AU - McKinley, Trevelyan J

AU - Overton, Christopher E

AU - Panovska-Griffiths, Jasmina

AU - Pellis, Lorenzo

AU - Probert, Will

AU - Shea, Katriona

AU - Villela, Daniel

AU - Vernon, Ian

N1 - DDA, 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.

PY - 2022/3

Y1 - 2022/3

N2 - 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.

AB - 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.

KW - Forecasting

KW - Pandemics

KW - Uncertainty

U2 - 10.1016/j.epidem.2022.100547

DO - 10.1016/j.epidem.2022.100547

M3 - Article

C2 - 35180542

SN - 1878-0067

VL - 38

JO - Epidemics

JF - Epidemics

M1 - 100547

ER -

Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling

Abstract

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Keywords

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