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Modelling: Understanding pandemics and how to control them.
Marion, Glenn; Hadley, Liza; Isham, Valerie; Mollison, Denis; Panovska-Griffiths, Jasmina; Pellis, Lorenzo; Tomba, Gianpaolo Scalia; Scarabel, Francesca; Swallow, Ben; Trapman, Pieter; Villela, Daniel.
Affiliation
  • Marion G; Biomathematics and Statistics Scotland, Edinburgh, UK; Scottish COVID-19 Response Consortium, UK. Electronic address: glenn.marion@bioss.ac.uk.
  • Hadley L; Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, UK.
  • Isham V; Department of Statistical Science, University College London, UK.
  • Mollison D; Department of Actuarial Mathematics and Statistics, Heriot-Watt University, UK.
  • Panovska-Griffiths J; The Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK; The Queen's College, Oxford University, UK.
  • Pellis L; Department of Mathematics, University of Manchester, UK; The Alan Turing Institute, London, UK; Joint UNIversities Pandemic and Epidemiological Research, UK.
  • Tomba GS; Department of Mathematics, University of Rome Tor Vergata, Rome, Italy.
  • Scarabel F; Department of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; CDLab - Computational Dynamics Laboratory, Department of Mathematics, Computer Science and Physics, University of Udine, Italy.
  • Swallow B; Scottish COVID-19 Response Consortium, UK; School of Mathematics and Statistics, University of Glasgow, UK.
  • Trapman P; Department of Mathematics, Stockholm University, Stockholm, Sweden.
  • Villela D; Program of Scientific Computing, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
Epidemics ; 39: 100588, 2022 06.
Article in En | MEDLINE | ID: mdl-35679714
ABSTRACT
New disease challenges, societal demands and better or novel types of data, drive innovations in the structure, formulation and analysis of epidemic models. Innovations in modelling can lead to new insights into epidemic processes and better use of available data, yielding improved disease control and stimulating collection of better data and new data types. Here we identify key challenges for the structure, formulation, analysis and use of mathematical models of pathogen transmission relevant to current and future pandemics.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Pandemics / Models, Theoretical Language: En Journal: Epidemics Year: 2022 Document type: Article
Fulltext
Add to My VHL
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PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Pandemics / Models, Theoretical Language: En Journal: Epidemics Year: 2022 Document type: Article