Modelling: Understanding pandemics and how to control them.

Models, Theoretical Pandemics / prevention & control
Behaviour and multi-scale transmission dynamics Infectious disease models Pathogen dynamics Value of information studies Within, host dynamics

Journal

Epidemics
ISSN: 1878-0067
Titre abrégé: Epidemics
Pays: Netherlands
ID NLM: 101484711

Informations de publication

Date de publication:
06 2022
Historique:
received: 10 09 2021
revised: 22 03 2022
accepted: 26 05 2022
pubmed: 10 6 2022
medline: 16 6 2022
entrez: 9 6 2022
Statut: ppublish

Résumé

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.

Identifiants

DOI: 10.1016/j.epidem.2022.100588 PMID: 35679714
pubmed: 35679714
pii: S1755-4365(22)00035-4
doi: 10.1016/j.epidem.2022.100588
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

100588

Subventions

Organisme : Medical Research Council
ID : MR/V038613/1
Pays : United Kingdom

Informations de copyright

Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.

Auteurs

Glenn Marion (G)

Biomathematics and Statistics Scotland, Edinburgh, UK; Scottish COVID-19 Response Consortium, UK. Electronic address: glenn.marion@bioss.ac.uk.

Liza Hadley (L)

Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, UK.

Valerie Isham (V)

Department of Statistical Science, University College London, UK.

Denis Mollison (D)

Department of Actuarial Mathematics and Statistics, Heriot-Watt University, UK.

Jasmina Panovska-Griffiths (J)

The Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK; The Queen's College, Oxford University, UK.

Lorenzo Pellis (L)

Department of Mathematics, University of Manchester, UK; The Alan Turing Institute, London, UK; Joint UNIversities Pandemic and Epidemiological Research, UK.

Gianpaolo Scalia Tomba (GS)

Department of Mathematics, University of Rome Tor Vergata, Rome, Italy.

Francesca 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.

Ben Swallow (B)

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

Pieter Trapman (P)

Department of Mathematics, Stockholm University, Stockholm, Sweden.

Daniel Villela (D)

Program of Scientific Computing, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.

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Classifications MeSH

questionsmedicales.fr › Techniques d'investigation › Modèles théoriques › Modelling: Understanding pandemics and how to...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Épidémies de maladies › Épidémies › Pandémies › Modelling: Understanding pandemics and how to...