Challenges for modelling interventions for future pandemics

Epidemics

Jasmina Panovska-Griffiths¹  Ganna Rozhnova²  Ben Ashby³  Robin N. Thompson⁴  Matthew Quaife⁴  Michael J. Tildesley⁵  Helena B. Stage⁶  Christopher E. Overton⁷  Elizabeth Fearon⁸  Lorenzo Pellis⁹  Ben Swallow⁹  Mirjam E. Kretzschmar^1,10  Daniel Villela^1,11  Francesca Scarabel^1,12 

[1] Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, UK;Clinical Data Science Unit, Manchester University NHS Foundation Trust, UK;Correspondence to: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584CX Utrecht, The Netherlands.;Joint UNIversities Pandemic and Epidemiological Research, UK;The Alan Turing Institute, London, UK;The Queen’s College, University of Oxford, Oxford, UK;Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK;Department of Mathematical Sciences, University of Bath, Bath BA2 7AY, UK;Department of Mathematics, University of Manchester, UK;Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands;TB Modelling Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, UK;The Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK;

关键词: Mathematical models;    Pandemics;    Pharmaceutical interventions;    Non-pharmaceutical interventions;    Policy support;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown