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Modelling : understanding pandemics and how to control them

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dc.contributor.authorMarion, Glenn
dc.contributor.authorHadley, Liza
dc.contributor.authorIsham, Valerie
dc.contributor.authorMollison, Denis
dc.contributor.authorPanovska-Griffiths, Jasmina
dc.contributor.authorPellis, Lorenzo
dc.contributor.authorTomba, Gianpaolo Scalia
dc.contributor.authorScarabel, Francesca
dc.contributor.authorSwallow, Ben
dc.contributor.authorTrapman, Pieter
dc.contributor.authorVillela, Daniel
dc.date.accessioned2022-09-28T11:30:19Z
dc.date.available2022-09-28T11:30:19Z
dc.date.issued2022-06-06
dc.identifier281140563
dc.identifier36236e74-6eaa-415d-9366-f92b5f229a2c
dc.identifier000810717200008
dc.identifier85131903235
dc.identifier35679714
dc.identifier.citationMarion , G , Hadley , L , Isham , V , Mollison , D , Panovska-Griffiths , J , Pellis , L , Tomba , G S , Scarabel , F , Swallow , B , Trapman , P & Villela , D 2022 , ' Modelling : understanding pandemics and how to control them ' , Epidemics , vol. 39 , 100588 . https://doi.org/10.1016/j.epidem.2022.100588en
dc.identifier.issn1755-4365
dc.identifier.otherORCID: /0000-0002-0227-2160/work/118411966
dc.identifier.urihttps://hdl.handle.net/10023/26085
dc.descriptionFunding: This work was supported by Engineering and Physical Sciences Research Council (EPSRC) grant no. EP/R014604/1. G.M. is supported by the Scottish Government's Rural and Environment Science and Analytical Services Division (RESAS). L.H. is funded by the Wellcome Trust, UK (block grant no. RG92770). L.P. is funded by the Wellcome Trust, UK and the Royal Society, UK (grant no. 202562/Z/16/Z). L.P. and F.S. are supported by the UK Research and Innovation (UKRI) through the JUNIPER modelling consortium (grant no. MR/V038613/1). L.P. is also supported by The Alan Turing Institute for Data Science and Artificial Intelligence. Daniel Villela is a fellow from National Council for Scientific and Technological Development, Brazil (Ref. 309569/2019–2, 441057/2020–9). JPG's work is supported by funding from the UK Health Security Agency and the UK Department of Health and Social Care.en
dc.description.abstractNew 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.
dc.format.extent13
dc.format.extent2928651
dc.language.isoeng
dc.relation.ispartofEpidemicsen
dc.subjectInfectious disease modelsen
dc.subjectBehaviour and multi-scale transmission dynamicsen
dc.subjectWithin, host dynamicsen
dc.subjectPathogen dynamicsen
dc.subjectValue of information studiesen
dc.subjectRA0421 Public health. Hygiene. Preventive Medicineen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectT-NDASen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subjectMCCen
dc.subject.lccRA0421en
dc.subject.lccQA75en
dc.titleModelling : understanding pandemics and how to control themen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.identifier.doi10.1016/j.epidem.2022.100588
dc.description.statusPeer revieweden


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