Modelling: Understanding pandemics and how to control them
Glenn Marion,
Liza Hadley,
Valerie Isham,
Denis Mollison,
Jasmina Panovska-Griffiths,
Lorenzo Pellis,
Gianpaolo Scalia Tomba,
Francesca Scarabel,
Ben Swallow,
Pieter Trapman,
Daniel Villela
Affiliations
Glenn Marion
Biomathematics and Statistics Scotland, Edinburgh, UK; Scottish COVID-19 Response Consortium, UK; Correspondence to: Biomathematics and Statistics Scotland, James Clerk Maxwell Building, The King's Buildings, Peter Guthrie Tait Road, Edinburgh, EH9 3FD, UK.
Liza Hadley
Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, UK
Valerie Isham
Department of Statistical Science, University College London, UK
Denis Mollison
Department of Actuarial Mathematics and Statistics, Heriot-Watt University, UK
Jasmina Panovska-Griffiths
The Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK; The Queen's College, Oxford University, UK
Lorenzo Pellis
Department of Mathematics, University of Manchester, UK; The Alan Turing Institute, London, UK; Joint UNIversities Pandemic and Epidemiological Research, UK
Gianpaolo Scalia Tomba
Department of Mathematics, University of Rome Tor Vergata, Rome, Italy
Francesca Scarabel
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
Scottish COVID-19 Response Consortium, UK; School of Mathematics and Statistics, University of Glasgow, UK
Pieter Trapman
Department of Mathematics, Stockholm University, Stockholm, Sweden
Daniel Villela
Program of Scientific Computing, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
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.
