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Challenges on the interaction of models and policy for pandemic control

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Hadley_2021_Epidemics_Challenges_interaction_CC.pdf (944.2Kb)
Date
01/12/2021
Author
Hadley, Liza
Challenor, Peter
Dent, Chris
Isham, Valerie
Mollison, Denis
Robertson, Duncan A.
Swallow, Ben
Webb, Cerian R.
Keywords
Modelling
Policy
Communication
Cooperation
Pandemic
RA0421 Public health. Hygiene. Preventive Medicine
QA Mathematics
T-NDAS
SDG 3 - Good Health and Well-being
MCC
Metadata
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Abstract
The COVID-19 pandemic has seen infectious disease modelling at the forefront of government decision-making. Models have been widely used throughout the pandemic to estimate pathogen spread and explore the potential impact of different intervention strategies. Infectious disease modellers and policymakers have worked effectively together, but there are many avenues for progress on this interface. In this paper, we identify and discuss seven broad challenges on the interaction of models and policy for pandemic control. We then conclude with suggestions and recommendations for the future.
Citation
Hadley , L , Challenor , P , Dent , C , Isham , V , Mollison , D , Robertson , D A , Swallow , B & Webb , C R 2021 , ' Challenges on the interaction of models and policy for pandemic control ' , Epidemics , vol. 37 , 100499 . https://doi.org/10.1016/j.epidem.2021.100499
Publication
Epidemics
Status
Peer reviewed
DOI
https://doi.org/10.1016/j.epidem.2021.100499
ISSN
1755-4365
Type
Journal article
Rights
Copyright © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
Description
Funding: The authors would like to thank the Isaac Newton Institute for Mathematical Sciences, Cambridge, for support during the Infectious Dynamics of Pandemics programme where work on this paper was undertaken. This work was supported by EPSRC grant no. EP/R014604/1. L.H. is supported by the Wellcome Trust (block grant no. RG92770). C.D. acknowledges the University of Edinburgh’s Data Driven Innovation Programme under Scottish Funding Council support, the Alan Turing Institute sponsored ‘Managing Uncertainty in Government Modelling’ project, and EPSRC (grant no. EP/P001173/1). B.S. acknowledges the Scottish Covid-19 Response Consortium.
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/26088

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