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 journalArticlepeer-review

21 Citations (SciVal)


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 languageEnglish
Article number100546
Early online date11 Feb 2022
Publication statusPublished - 31 Mar 2022

Bibliographical note

Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.


Dive into the research topics of 'Challenges for modelling interventions for future pandemics'. Together they form a unique fingerprint.

Cite this