Challenges for modelling interventions for future pandemics

Mirjam E Kretzschmar; Ben Ashby; Elizabeth Fearon; Christopher E Overton; Jasmina Panovska-Griffiths; Lorenzo Pellis; Matthew Quaife; Ganna Rozhnova; Francesca Scarabel; Helena B Stage; Ben Swallow; Robin N Thompson; Michael J Tildesley; Daniel Villela

doi:10.1016/j.epidem.2022.100546

Challenges for modelling interventions for future pandemics

Mirjam E Kretzschmar, Ben Ashby, Elizabeth Fearon, Christopher E Overton, Jasmina Panovska-Griffiths, Lorenzo Pellis, Matthew Quaife, Ganna Rozhnova, Francesca Scarabel, Helena B Stage, Ben Swallow, Robin N Thompson, Michael J Tildesley, Daniel Villela

Research output: Contribution to journal › Article › peer-review

45 Citations (SciVal)

Abstract

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.

Original language	English
Article number	100546
Journal	Epidemics
Volume	38
Early online date	11 Feb 2022
DOIs	https://doi.org/10.1016/j.epidem.2022.100546
Publication status	Published - 31 Mar 2022

Bibliographical note

Funding

This work was supported by the Isaac Newton Institute (EPSRC grant no. EP/R014604/1 ). MEK was supported by grants from The Netherlands Organisation for Health Research and Development (ZonMw), grant number 10430022010001 , and grant number 91216062 , and by the H2020 Project 101003480 (CORESMA). RNT was supported by the UKRI , grant number EP/V053507/1 . GR was supported by Fundação para a Ciência e a Tecnologia (FCT) project reference 131_596787873 . and by the VERDI project 101045989 funded by the European Union . LP and CO are funded by the Wellcome Trust and the Royal Society (grant 202562/Z/16/Z ). LP is also supported by the UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1 ) and by The Alan Turing Institute for Data Science and Artificial Intelligence . HBS is funded by the Wellcome Trust and Royal Society (202562/Z/16/Z), and the Alexander von Humboldt Foundation . DV had support from the National Council for Scientific and Technological Development of Brazil (CNPq - Refs. 441057/2020-9 , 424141/2018-3 , 309569/2019-2 ). FS is supported by the UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1). EF is supported by UKRI (Medical Research Council)/Department of Health and Social Care (National Insitute of Health Research) MR/V028618/1 . JPG's work was supported by funding from the UK Health Security Agency and the UK Department of Health and Social Care . This funder had no role in the study design, data analysis, data interpretation, or writing of the report. The views expressed in this article are those of the authors and not necessarily those of the UK Health Security Agency or the UK Department of Health and Social Care. This work was supported by the Isaac Newton Institute (EPSRC grant no. EP/R014604/1). MEK was supported by grants from The Netherlands Organisation for Health Research and Development (ZonMw), grant number 10430022010001, and grant number 91216062, and by the H2020 Project 101003480 (CORESMA). RNT was supported by the UKRI, grant number EP/V053507/1. GR was supported by Funda??o para a Ci?ncia e a Tecnologia (FCT) project reference 131_596787873. and by the VERDI project 101045989 funded by the European Union. LP and CO are funded by the Wellcome Trust and the Royal Society (grant 202562/Z/16/Z). LP is also supported by the UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1) and by The Alan Turing Institute for Data Science and Artificial Intelligence. HBS is funded by the Wellcome Trust and Royal Society (202562/Z/16/Z), and the Alexander von Humboldt Foundation. DV had support from the National Council for Scientific and Technological Development of Brazil (CNPq - Refs. 441057/2020-9, 424141/2018-3, 309569/2019-2). FS is supported by the UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1). EF is supported by UKRI (Medical Research Council)/Department of Health and Social Care (National Insitute of Health Research) MR/V028618/1. JPG's work was supported by funding from the UK Health Security Agency and the UK Department of Health and Social Care. This funder had no role in the study design, data analysis, data interpretation, or writing of the report. The views expressed in this article are those of the authors and not necessarily those of the UK Health Security Agency or the UK Department of Health and Social Care.

Access to Document

10.1016/j.epidem.2022.100546Licence: CC BY-NC-ND

The eco-evolutionary dynamics of age-specific resistance to infectious disease
Ashby, B. (PI)
Natural Environment Research Council
1/06/20 → 31/01/24
Project: Research council
Fellowship Ben Ashby - Host-parasite Coevolution in Complex Communities
Ashby, B. (PI)
Natural Environment Research Council
1/10/16 → 30/09/22
Project: Research council

Cite this

@article{bf905834033c404eba8d3d9db270e145,

title = "Challenges for modelling interventions for future pandemics",

abstract = "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.",

author = "Kretzschmar, {Mirjam E} and Ben Ashby and Elizabeth Fearon and Overton, {Christopher E} and Jasmina Panovska-Griffiths and Lorenzo Pellis and Matthew Quaife and Ganna Rozhnova and Francesca Scarabel and Stage, {Helena B} and Ben Swallow and Thompson, {Robin N} and Tildesley, {Michael J} and Daniel Villela",

year = "2022",

month = mar,

day = "31",

doi = "10.1016/j.epidem.2022.100546",

language = "English",

volume = "38",

journal = "Epidemics",

issn = "1878-0067",

publisher = "Elsevier",

}

TY - JOUR

T1 - Challenges for modelling interventions for future pandemics

AU - Kretzschmar, Mirjam E

AU - Ashby, Ben

AU - Fearon, Elizabeth

AU - Overton, Christopher E

AU - Panovska-Griffiths, Jasmina

AU - Pellis, Lorenzo

AU - Quaife, Matthew

AU - Rozhnova, Ganna

AU - Scarabel, Francesca

AU - Stage, Helena B

AU - Swallow, Ben

AU - Thompson, Robin N

AU - Tildesley, Michael J

AU - Villela, Daniel

PY - 2022/3/31

Y1 - 2022/3/31

N2 - 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.

AB - 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.

U2 - 10.1016/j.epidem.2022.100546

DO - 10.1016/j.epidem.2022.100546

M3 - Article

C2 - 35183834

SN - 1878-0067

VL - 38

JO - Epidemics

JF - Epidemics

M1 - 100546

ER -

Challenges for modelling interventions for future pandemics

Abstract

Bibliographical note

Funding

Access to Document

Fingerprint

Projects

The eco-evolutionary dynamics of age-specific resistance to infectious disease

Fellowship Ben Ashby - Host-parasite Coevolution in Complex Communities

Cite this