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Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling.
Swallow, Ben; Birrell, Paul; Blake, Joshua; Burgman, Mark; Challenor, Peter; Coffeng, Luc E; Dawid, Philip; De Angelis, Daniela; Goldstein, Michael; Hemming, Victoria; Marion, Glenn; McKinley, Trevelyan J; Overton, Christopher E; Panovska-Griffiths, Jasmina; Pellis, Lorenzo; Probert, Will; Shea, Katriona; Villela, Daniel; Vernon, Ian.
  • Swallow B; School of Mathematics and Statistics, University of Glasgow, Glasgow, UK; Scottish COVID-19 Response Consortium, UK. Electronic address: ben.swallow@glasgow.ac.uk.
  • Birrell P; Analytics & Data Science, UKHSA, UK; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
  • Blake J; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
  • Burgman M; Centre for Environmental Policy, Imperial College London, London, UK.
  • Challenor P; The Alan Turing Institute, London, UK; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.
  • Coffeng LE; Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
  • Dawid P; Statistical Laboratory, University of Cambridge, Cambridge, UK.
  • De Angelis D; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK; Joint UNIversities Pandemic and Epidemiological Research, UK.
  • Goldstein M; Department of Mathematical Sciences, Durham University, Stockton Road, Durham, UK.
  • Hemming V; Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, Canada.
  • Marion G; Scottish COVID-19 Response Consortium, UK; Biomathematics and Statistics Scotland, Edinburgh, UK.
  • McKinley TJ; College of Medicine and Health, University of Exeter, Exeter, UK; Joint UNIversities Pandemic and Epidemiological Research, UK.
  • Overton CE; 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.
  • Panovska-Griffiths J; The Big Data Institute, University of Oxford, Oxford, UK; The Queen's College, University of Oxford, Oxford, UK.
  • Pellis L; Department of Mathematics, University of Manchester, Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; The Alan Turing Institute, London, UK.
  • Probert W; The Big Data Institute, University of Oxford, Oxford, UK.
  • Shea K; Department of Biology and Centre for Infectious Disease Dynamics, The Pennsylvania State University, PA 16802, USA.
  • Villela D; Program of Scientific Computing, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
  • Vernon I; Department of Mathematical Sciences, Durham University, Stockton Road, Durham, UK.
Epidemics ; 38: 100547, 2022 03.
Article in English | MEDLINE | ID: covidwho-1700614
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics Type of study: Prognostic study Language: English Journal: Epidemics Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics Type of study: Prognostic study Language: English Journal: Epidemics Year: 2022 Document Type: Article