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Workshop 02 - 9:00 to 12:30 pm
HPC Technologies and Health
Modelling the spread of infectious diseases
By Simon Cauchemez, Infectious disease epidemiologist and modeller, Institut Pasteur
During the emergence of infectious diseases such as COVID-19, it is essential to perform timely and robust assessment of risks to support decision making. However, this is challenging in a context where epidemics have complex non-linear dynamics and the underlying epidemic process is imperfectly observed. Mathematical models have a key role to play to address these challenges. In this talk, considering examples from COVID-19 and pandemic influenza, I will present the different types of models that are used to study epidemics, discuss the trade-off between complexity and parsimony and showcase the use of mathematical models during the COVID-19 pandemic. The revolution in this field has come from the development of statistical methods to calibrate relatively parsimonious models to data, rather than the development of ever more complex models.
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Biography: After completing his PhD in 2005 in the INSERM Unit of Alain-Jacques Valleron, Simon Cauchemez worked for 8 years in the Department of Infectious Disease Epidemiology at Imperial College London. In 2013, he moved back to France to create the Mathematical Modelling of Infectious Diseases Unit at Institut Pasteur, that he currently leads. His main research interest is to develop statistical and mathematical methods to increase our understanding of how pathogens spread in populations, assess the impact of interventions, support policy making and optimize control strategies. His approach is multidisciplinary, looking at infectious diseases through multiple perspectives (epidemiology, statistics, modelling, surveillance, public health, policy making, microbiology), multiple scales (intra-host, person-to-person transmission, spread at the local/national/international level) and multiple data streams (epidemiological, demographic, climatic, genomic, social media). He is a member of the French COVID-19 Scientific Council. |
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