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Cited by Other Article(s)

Swallow B, Birrell P, Blake J, Burgman M, Challenor P, Coffeng LE, Dawid P, De Angelis D, Goldstein M, Hemming V, Marion G, McKinley TJ, Overton CE, Panovska-Griffiths J, Pellis L, Probert W, Shea K, Villela D, Vernon I. Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling. Epidemics 2022;38:100547. [PMID: 35180542 PMCID: PMC7612598 DOI: 10.1016/j.epidem.2022.100547] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 12/22/2021] [Accepted: 02/09/2022] [Indexed: 12/15/2022] Open

Affiliation(s)

Ben Swallow School of Mathematics and Statistics, University of Glasgow, Glasgow, UK; Scottish COVID-19 Response Consortium, 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

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Glennie R, Adam T, Leos‐Barajas V, Michelot T, Photopoulou T, McClintock BT. Hidden Markov Models: Pitfalls and Opportunities in Ecology. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13801] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]