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

UK Health Security Agency

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

21 Citations (Scopus)

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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.

Original language	English
Article number	100547
Journal	Epidemics
Volume	38
Early online date	10 Feb 2022
DOIs	https://doi.org/10.1016/j.epidem.2022.100547
Publication status	Published - Mar 2022

Bibliographical note

Funding Information: 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. This funder had no role in the study design, data analysis, data interpretation, or writing of the report. The views expressed in this article are those of the authors and not necessarily those of the UK Health Security Agency or the UK Department of Health and Social Care.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

The authors would like to thank the Isaac Newton Institute for Mathematical Sciences, Cambridge, for support during the Infectious Dynamics of Pandemics programme where work on this paper was undertaken. This work was supported by EPSRC Grant no. EP/R014604/1.

LEC is a member of the Dutch Covid-19 Monitoring Consortium, which is funded by ZonMw (https://www.zonmw.nl, Grant 10430022010001). Daniel Villela is a fellow from National Council for Scientific and Technological Development (Ref. 309569/2019-2, 441057/2020-9). Katriona Shea acknowledges NSF COVID-19 RAPID Award 2028301. TJM is supported by an "Expanding Excellence in England" award from Research England and UKRI Grants: EP/V051555/1 and MR/V038613/1 (JUNIPER Consortium). LP and CO are funded by the Wellcome Trust and the Royal Society (Grant 202562/Z/16/Z). LP is also supported by the UKRI through the JUNIPER Modelling Consortium (Grant no. MR/V038613/1) and by The Alan Turing Institute for Data Science and Artificial Intelligence.

G.M. is supported by the Scottish Government’s Rural and Environment Science and Analytical Services Division (RESAS).

The SCRC is supported by the Royal Society Rapid Assistance in Modelling the Pandemic: RAMP Initiative.

Open Access: This is an open access article under the CC BY-NC-ND license.

Publisher Copyright: © 2022 The Authors. Published by Elsevier B.V.

Citation: Swallow, Ben, et al. "Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling:(IDP Challenges Series)." Epidemics (2022): 100547.

DOI: https://doi.org/10.1016/j.epidem.2022.100547

Keywords

Expert elicitation
Pandemic modelling
Statistical estimation
Uncertainty quantification

UN SDGs

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

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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.",

keywords = "Expert elicitation, Pandemic modelling, Statistical estimation, Uncertainty quantification",

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 = "Funding Information: 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{\textquoteright}s work was supported by funding from the UK Health Security Agency and the UK Department of Health and Social Care. This funder had no role in the study design, data analysis, data interpretation, or writing of the report. The views expressed in this article are those of the authors and not necessarily those of the UK Health Security Agency or the UK Department of Health and Social Care. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors would like to thank the Isaac Newton Institute for Mathematical Sciences, Cambridge, for support during the Infectious Dynamics of Pandemics programme where work on this paper was undertaken. This work was supported by EPSRC Grant no. EP/R014604/1. LEC is a member of the Dutch Covid-19 Monitoring Consortium, which is funded by ZonMw (https://www.zonmw.nl, Grant 10430022010001). Daniel Villela is a fellow from National Council for Scientific and Technological Development (Ref. 309569/2019-2, 441057/2020-9). Katriona Shea acknowledges NSF COVID-19 RAPID Award 2028301. TJM is supported by an {"}Expanding Excellence in England{"} award from Research England and UKRI Grants: EP/V051555/1 and MR/V038613/1 (JUNIPER Consortium). LP and CO are funded by the Wellcome Trust and the Royal Society (Grant 202562/Z/16/Z). LP is also supported by the UKRI through the JUNIPER Modelling Consortium (Grant no. MR/V038613/1) and by The Alan Turing Institute for Data Science and Artificial Intelligence. G.M. is supported by the Scottish Government{\textquoteright}s Rural and Environment Science and Analytical Services Division (RESAS). The SCRC is supported by the Royal Society Rapid Assistance in Modelling the Pandemic: RAMP Initiative. Open Access: This is an open access article under the CC BY-NC-ND license. Publisher Copyright: {\textcopyright} 2022 The Authors. Published by Elsevier B.V. Citation: Swallow, Ben, et al. {"}Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling:(IDP Challenges Series).{"} Epidemics (2022): 100547. DOI: https://doi.org/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

N1 - Funding Information: 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. This funder had no role in the study design, data analysis, data interpretation, or writing of the report. The views expressed in this article are those of the authors and not necessarily those of the UK Health Security Agency or the UK Department of Health and Social Care. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors would like to thank the Isaac Newton Institute for Mathematical Sciences, Cambridge, for support during the Infectious Dynamics of Pandemics programme where work on this paper was undertaken. This work was supported by EPSRC Grant no. EP/R014604/1. LEC is a member of the Dutch Covid-19 Monitoring Consortium, which is funded by ZonMw (https://www.zonmw.nl, Grant 10430022010001). Daniel Villela is a fellow from National Council for Scientific and Technological Development (Ref. 309569/2019-2, 441057/2020-9). Katriona Shea acknowledges NSF COVID-19 RAPID Award 2028301. TJM is supported by an "Expanding Excellence in England" award from Research England and UKRI Grants: EP/V051555/1 and MR/V038613/1 (JUNIPER Consortium). LP and CO are funded by the Wellcome Trust and the Royal Society (Grant 202562/Z/16/Z). LP is also supported by the UKRI through the JUNIPER Modelling Consortium (Grant no. MR/V038613/1) and by The Alan Turing Institute for Data Science and Artificial Intelligence. G.M. is supported by the Scottish Government’s Rural and Environment Science and Analytical Services Division (RESAS). The SCRC is supported by the Royal Society Rapid Assistance in Modelling the Pandemic: RAMP Initiative. Open Access: This is an open access article under the CC BY-NC-ND license. Publisher Copyright: © 2022 The Authors. Published by Elsevier B.V. Citation: Swallow, Ben, et al. "Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling:(IDP Challenges Series)." Epidemics (2022): 100547. DOI: https://doi.org/10.1016/j.epidem.2022.100547

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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 - Expert elicitation

KW - Pandemic modelling

KW - Statistical estimation

KW - Uncertainty quantification

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DO - 10.1016/j.epidem.2022.100547

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SN - 1755-4365

VL - 38

JO - Epidemics

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M1 - 100547

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Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling

Abstract

Bibliographical note

Keywords

UN SDGs

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