Mathematical descriptions of infectious disease outbreaks are fundamental to understanding how transmission occurs. Reductively, two approaches are used: individual based simulators and governing equation models, and both approaches have a multitude of pros and cons. In this talk I will connect these two worlds via general branching processes. I will discuss (at a high level) the rather beautiful mathematics that arises from these branching processes and how these can help us understand the assumptions underpinning mathematical models for infectious disease. I will then explain how this new maths can help us understand uncertainty better, and show some simple examples. This talk will be a little technical, but I will focus as much as possible on intuition and the big picture.

Samir Bhatt is a Professor of Machine Learning and Public Health at the University of Copenhagen, and a Professor of Statistics and Public Health at Imperial College London. He did his PhD and subsequent post doctoral research in statistical genetics and geostatistics at the Department of Zoology, Oxford.  For the past decade he has worked on infectious diseases. Career highlights include tracking the origins of the 2009 Swine Flu pandemic, the first paper outlining the global burden of Dengue, the first paper on the impact of malaria control since the year 2000, and the first paper on housing and disease in Sub-Saharan Africa. More recently he has been heavily involved in research around the COVID-19 pandemic both in a research and advisory capacity. Samir is interested in the interface of mathematics and epidemiology and generally loves collaborations on any technical topic!