Challenges for modelling interventions for future pandemics
Mathematical modelling and statistical inference provide a framework to evaluate different non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has been widely used during the COVID-19 pandemic. In this paper, lessons learned from this and previous epidemics are used...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2022
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| _version_ | 1797089036241731584 |
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| author | Kretzschmar, ME Ashby, B Fearon, E Overton, CE Panovska-Griffiths, J Pellis, L Quaife, M Rozhnova, G Scarabel, F Stage, HB Swallow, B Thompson, RN Tildesley, MJ Villela, D |
| author_facet | Kretzschmar, ME Ashby, B Fearon, E Overton, CE Panovska-Griffiths, J Pellis, L Quaife, M Rozhnova, G Scarabel, F Stage, HB Swallow, B Thompson, RN Tildesley, MJ Villela, D |
| author_sort | Kretzschmar, ME |
| collection | OXFORD |
| description | Mathematical modelling and statistical inference provide a framework to evaluate different non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has been widely used during the COVID-19 pandemic. In this paper, lessons learned from this and previous epidemics are used to highlight the challenges for future pandemic control. We consider the availability and use of data, as well as the need for correct parameterisation and calibration for different model frameworks. We discuss challenges that arise in describing and distinguishing between different interventions, within different modelling structures, and allowing both within and between host dynamics. We also highlight challenges in modelling the health economic and political aspects of interventions. Given the diversity of these challenges, a broad variety of interdisciplinary expertise is needed to address them, combining mathematical knowledge with biological and social insights, and including health economics and communication skills. Addressing these challenges for the future requires strong cross-disciplinary collaboration together with close communication between scientists and policy makers. |
| first_indexed | 2024-03-07T02:58:42Z |
| format | Journal article |
| id | oxford-uuid:b033cb72-b977-4558-9e15-a0e7cd37c30c |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T02:58:42Z |
| publishDate | 2022 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:b033cb72-b977-4558-9e15-a0e7cd37c30c2022-03-27T03:54:48ZChallenges for modelling interventions for future pandemicsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:b033cb72-b977-4558-9e15-a0e7cd37c30cEnglishSymplectic ElementsElsevier2022Kretzschmar, MEAshby, BFearon, EOverton, CEPanovska-Griffiths, JPellis, LQuaife, MRozhnova, GScarabel, FStage, HBSwallow, BThompson, RNTildesley, MJVillela, DMathematical modelling and statistical inference provide a framework to evaluate different non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has been widely used during the COVID-19 pandemic. In this paper, lessons learned from this and previous epidemics are used to highlight the challenges for future pandemic control. We consider the availability and use of data, as well as the need for correct parameterisation and calibration for different model frameworks. We discuss challenges that arise in describing and distinguishing between different interventions, within different modelling structures, and allowing both within and between host dynamics. We also highlight challenges in modelling the health economic and political aspects of interventions. Given the diversity of these challenges, a broad variety of interdisciplinary expertise is needed to address them, combining mathematical knowledge with biological and social insights, and including health economics and communication skills. Addressing these challenges for the future requires strong cross-disciplinary collaboration together with close communication between scientists and policy makers. |
| spellingShingle | Kretzschmar, ME Ashby, B Fearon, E Overton, CE Panovska-Griffiths, J Pellis, L Quaife, M Rozhnova, G Scarabel, F Stage, HB Swallow, B Thompson, RN Tildesley, MJ Villela, D Challenges for modelling interventions for future pandemics |
| title | Challenges for modelling interventions for future pandemics |
| title_full | Challenges for modelling interventions for future pandemics |
| title_fullStr | Challenges for modelling interventions for future pandemics |
| title_full_unstemmed | Challenges for modelling interventions for future pandemics |
| title_short | Challenges for modelling interventions for future pandemics |
| title_sort | challenges for modelling interventions for future pandemics |
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