The event will be hosted virtually via Microsoft Teams. Please register to receive the joining link.
Mathematical modelling became a central tool to control the coronavirus in many countries, for example in the UK. Mathematical models can be used to forecast the transmission and spread of COVID-19, including the magnitude and timing of the peak of the outbreak, as well as how these may vary with the use of different intervention strategies. While the predictions generated by mathematical modelling have played an important role in policy development and outbreak management, mathematical models are subject to several limitations. Mathematical models are built around a number of assumptions and conditions, and represent a simplification of reality. One such simplifying assumption could be to represent every person as equally at risk from the virus. However, in reality different people face very different risks of infection, developing severe disease and dying from the disease, depending on factors such as socio-economic class, occupation, or access to soap and clean water. To what extent were such inequalities factored into the models that were used? To what extent is it possible to represent unequal risk distribution in a model?
This session examines the use of mathematical modelling in our outbreak response, what the models looked like in the beginning and how they evolved. To what extent were crucial inequalities taken into account? How can we do better? How can social epidemiology and infectious epidemiology be brought together to improve our response to infectious disease epidemics?
Dr. Sharmistha Mishra is an infectious disease physician and mathematical modeler. She holds a Canada Research Chair in Mathematical Modeling and Program Science, and is Assistant Professor at the Department of Medicine and Institute of Health Policy, Evaluation and Management at the University of Toronto. She is also a Clinical Scientist at the St Michael’s Hospital at the Department of Infectious Diseases at the University of Toronto.
After completing medical school and residency training (Internal Medicine, Infectious Diseases) at the University of Toronto, she obtained a Masters of Science degree in epidemiology and a Doctor of Philosophy in mathematical modelling at Imperial College London. She joined St. Michael’s Hospital in September 2014. She was also involved in the 2014-2015 Ebola response in Sierra Leone, as a consultant with the World Health Organization, from December 2014 to July 2015.
Dr Juliette Unwin is an Imperial College Research Fellow (ICRF) based in the MRC centre for Global Infectious Disease Analysis in the School of Public Health at Imperial College, London. She is interested in applying and developing novel methods for outbreak analysis to help inform policy makers in real time.
She is currently part of the Imperial College COVID-19 response team looking at real time modelling of Rt across Europe and the USA.