Modelling : understanding pandemics and how to control them
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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.
Marion , 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.2022.100588
Copyright © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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.
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