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

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

18 Scopus citations

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 (US)
Article number	100547
Journal	Epidemics
Volume	38
DOIs	https://doi.org/10.1016/j.epidem.2022.100547
State	Published - Mar 2022

All Science Journal Classification (ASJC) codes

Epidemiology
Parasitology
Microbiology
Public Health, Environmental and Occupational Health
Infectious Diseases
Virology

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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

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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.",

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 = "Publisher Copyright: {\textcopyright} 2022",

year = "2022",

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

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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

PY - 2022/3

Y1 - 2022/3

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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.

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JO - Epidemics

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Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling

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