Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling

Ben Swallow¹, Paul Birrell², Joshua Blake³, Mark Burgman⁴, Peter Challenor⁵, Luc E Coffeng⁶, Philip Dawid⁷, Daniela De Angelis⁸, Michael Goldstein⁹, Victoria Hemming¹⁰, Glenn Marion¹¹, Trevelyan J McKinley¹², Christopher E Overton¹³, Jasmina Panovska-Griffiths¹⁴, Lorenzo Pellis¹⁵, Will Probert¹⁶, Katriona Shea¹⁷, Daniel Villela¹⁸, Ian Vernon⁹

Affiliations

¹ School of Mathematics and Statistics, University of Glasgow, Glasgow, UK; Scottish COVID-19 Response Consortium, UK. Electronic address: ben.swallow@glasgow.ac.uk.
² Analytics & Data Science, UKHSA, UK; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
³ MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
⁴ Centre for Environmental Policy, Imperial College London, London, UK.
⁵ The Alan Turing Institute, London, UK; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.
⁶ Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
⁷ Statistical Laboratory, University of Cambridge, Cambridge, UK.
⁸ MRC Biostatistics Unit, University of Cambridge, Cambridge, UK; Joint UNIversities Pandemic and Epidemiological Research, UK.
⁹ Department of Mathematical Sciences, Durham University, Stockton Road, Durham, UK.
¹⁰ Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, Canada.
¹¹ Scottish COVID-19 Response Consortium, UK; Biomathematics and Statistics Scotland, Edinburgh, UK.
¹² College of Medicine and Health, University of Exeter, Exeter, UK; Joint UNIversities Pandemic and Epidemiological Research, UK.
¹³ Department of Mathematics, University of Manchester, Manchester, UK; Clinical Data Science Unit, Manchester University NHS Foundation Trust, Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK.
¹⁴ The Big Data Institute, University of Oxford, Oxford, UK; The Queen's College, University of Oxford, Oxford, UK.
¹⁵ Department of Mathematics, University of Manchester, Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; The Alan Turing Institute, London, UK.
¹⁶ The Big Data Institute, University of Oxford, Oxford, UK.
¹⁷ Department of Biology and Centre for Infectious Disease Dynamics, The Pennsylvania State University, PA 16802, USA.
¹⁸ Program of Scientific Computing, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.

PMID: 35180542
PMCID: PMC7612598
DOI: 10.1016/j.epidem.2022.100547

Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling

Ben Swallow et al. Epidemics. 2022 Mar.

. 2022 Mar:38:100547.

doi: 10.1016/j.epidem.2022.100547. Epub 2022 Feb 10.

Authors

Affiliations

¹ School of Mathematics and Statistics, University of Glasgow, Glasgow, UK; Scottish COVID-19 Response Consortium, UK. Electronic address: ben.swallow@glasgow.ac.uk.
² Analytics & Data Science, UKHSA, UK; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
³ MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
⁴ Centre for Environmental Policy, Imperial College London, London, UK.
⁵ The Alan Turing Institute, London, UK; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.
⁶ Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
⁷ Statistical Laboratory, University of Cambridge, Cambridge, UK.
⁸ MRC Biostatistics Unit, University of Cambridge, Cambridge, UK; Joint UNIversities Pandemic and Epidemiological Research, UK.
⁹ Department of Mathematical Sciences, Durham University, Stockton Road, Durham, UK.
¹⁰ Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, Canada.
¹¹ Scottish COVID-19 Response Consortium, UK; Biomathematics and Statistics Scotland, Edinburgh, UK.
¹² College of Medicine and Health, University of Exeter, Exeter, UK; Joint UNIversities Pandemic and Epidemiological Research, UK.
¹³ Department of Mathematics, University of Manchester, Manchester, UK; Clinical Data Science Unit, Manchester University NHS Foundation Trust, Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK.
¹⁴ The Big Data Institute, University of Oxford, Oxford, UK; The Queen's College, University of Oxford, Oxford, UK.
¹⁵ Department of Mathematics, University of Manchester, Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; The Alan Turing Institute, London, UK.
¹⁶ The Big Data Institute, University of Oxford, Oxford, UK.
¹⁷ Department of Biology and Centre for Infectious Disease Dynamics, The Pennsylvania State University, PA 16802, USA.
¹⁸ Program of Scientific Computing, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.

PMID: 35180542
PMCID: PMC7612598
DOI: 10.1016/j.epidem.2022.100547

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.

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Conflict of interest statement

Declaration of Competing Interest

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.

References

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Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling

Affiliations

Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling

Authors

Affiliations

Abstract

Conflict of interest statement

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources