For decades, mathematicians have developed tools for understanding the dynamics of transmissible infectious diseases. Many of the concepts introduced, such as the ‘R0 coefficient’, the basic reproduction number, have passed into common language. In epidemiology, as in many other domains, the trend today is towards more and more observations, at an ever finer level of granularity. Expectations are high, it is hoped that epidemic models will permit to determine optimal control strategies, assess the role played by certain factors (e.g. genetic traits, environmental conditions) and will be used for prediction purposes. In this webinar, we propose a brief tour of stochastic concepts and tools for mathematical epidemiology (probabilistic modeling and analysis, statistical inference, simulation), show how they can be put into concrete form through the description of real examples and try to explain how the epidemic datasets now available may lead to new mathematical elaborations and techniques.
Keywords: mathematical epidemiology, stochastic modeling/analysis, rare event analysis, ABC methods, HIV epidemics in presence of contact-tracing, graph-based models