TY  - JOUR
A1  - Kretzschmar, Mirjam E.
A1  - Ashby, Ben
A1  - Fearon, Elizabeth
A1  - Overton, Christopher E.
A1  - Panovska-Griffiths, Jasmina
A1  - Pellis, Lorenzo
A1  - Quaife, Matthew
A1  - Rozhnova, Ganna
A1  - Scarabel, Francesca
A1  - Stage, Helena B.
A1  - Swallow, Ben
A1  - Thompson, Robin N.
A1  - Tildesley, Michael J.
A1  - Villela, Daniel Campos
T1  - Challenges for modelling interventions for future pandemics
JF  - Epidemics
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.
KW  - Mathematical models
KW  - Pandemics
KW  - Pharmaceutical interventions
KW  - Non-pharmaceutical interventions
KW  - Policy support
Y1  - 2022
U6  - https://doi.org/10.1016/j.epidem.2022.100546
SN  - 1755-4365
SN  - 1878-0067
VL  - 38
PB  - Elsevier
CY  - Amsterdam
ER  -