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

^*Corresponding author for this work

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

35 Citations (Scopus)

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
DOIs	https://doi.org/10.1016/j.epidem.2022.100546
Publication status	Published - Mar 2022
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2022 The Authors

Keywords

Mathematical models
Non-pharmaceutical interventions
Pandemics
Pharmaceutical interventions
Policy support

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.epidem.2022.100546

Cite this

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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.",

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

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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

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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.

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Challenges for modelling interventions for future pandemics

Abstract

Bibliographical note

Keywords

UN SDGs

Access to Document

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