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 journalArticlepeer-review

13 Citations (Scopus)
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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.

Original languageEnglish
Article number100547
Early online date10 Feb 2022
Publication statusPublished - 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 (, 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.



  • Expert elicitation
  • Pandemic modelling
  • Statistical estimation
  • Uncertainty quantification


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