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 journalArticlepeer-review

11 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 languageEnglish (US)
Article number100547
JournalEpidemics
Volume38
DOIs
StatePublished - Mar 2022

All Science Journal Classification (ASJC) codes

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

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