Challenges for modelling interventions for future pandemics
- 1. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University
- 2. Department of Mathematical Sciences, University of Bath
- 3. Department of Global Health and Development and Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine
- 4. Department of Mathematics, University of Manchester; Joint UNIversities Pandemic and Epidemiological Research; Clinical Data Science Unit, Manchester University NHS Foundation Trust
- 5. The Big Data Institute, Nuffield Department of Medicine and the Queen's College, University of Oxford
- 6. Department of Mathematics, University of Manchester; f Joint UNIversities Pandemic and Epidemiological Research; The Alan Turing Institute, London
- 7. TB Modelling Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine
- 8. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University; BioISI—Biosystems & Integrative Sciences Institute, Faculdade de Ciˆencias, Universidade de Lisboa
- 9. Department of Mathematics, University of Manchester; Joint UNIversities Pandemic and Epidemiological Research; CDLab - Computational Dynamics Laboratory, Department of Mathematics, Computer Science and Physics, University of Udine
- 10. Department of Mathematics, University of Manchester; Joint UNIversities Pandemic and Epidemiological Research; University of Potsdam, Germany; Humboldt University of Berlin, Germany
- 11. School of Mathematics and Statistics, University of Glasgow; Scottish Covid-19 Response Consortium;
- 12. Joint UNIversities Pandemic and Epidemiological Research; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research and Mathematics Institute, University of Warwick
- 13. Program of Scientific Computing, Oswaldo Cruz Foundation, Rio de Janeiro
Description
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.