Epidemics (Mar 2022)
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
Affiliations
- Ben Swallow
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK; Scottish COVID-19 Response Consortium, UK; Correspondence to: School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ, UK.
- Paul Birrell
- Analytics & Data Science, UKHSA, UK; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Joshua Blake
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Mark Burgman
- Centre for Environmental Policy, Imperial College London, London, UK
- Peter Challenor
- The Alan Turing Institute, London, UK; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
- Luc E. Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Philip Dawid
- Statistical Laboratory, University of Cambridge, Cambridge, UK
- Daniela De Angelis
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK; Joint UNIversities Pandemic and Epidemiological Research, UK
- Michael Goldstein
- Department of Mathematical Sciences, Durham University, Stockton Road, Durham, UK
- Victoria Hemming
- Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, Canada
- Glenn Marion
- Scottish COVID-19 Response Consortium, UK; Biomathematics and Statistics Scotland, Edinburgh, UK
- Trevelyan J. McKinley
- College of Medicine and Health, University of Exeter, Exeter, UK; Joint UNIversities Pandemic and Epidemiological Research, UK
- Christopher E. Overton
- 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
- Jasmina Panovska-Griffiths
- The Big Data Institute, University of Oxford, Oxford, UK; The Queen’s College, University of Oxford, Oxford, UK
- Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; The Alan Turing Institute, London, UK
- Will Probert
- The Big Data Institute, University of Oxford, Oxford, UK
- Katriona Shea
- Department of Biology and Centre for Infectious Disease Dynamics, The Pennsylvania State University, PA 16802, USA
- Daniel Villela
- Program of Scientific Computing, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
- Ian Vernon
- Department of Mathematical Sciences, Durham University, Stockton Road, Durham, UK
- Journal volume & issue
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Vol. 38
p. 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.
