Why we couldn’t find the best disease control option even if we were omniscient
We can view many disease-spreading systems as networks of potentially-infectious connections between people, places, animals, or plants. We might then think of many disease-control problems as network optimisation questions, for example: given a network which are the very best nodes to vaccinate if we have a limited budget, or on which trade routes is it most important that we do extra biosecurity checks?
In this webinar, Dr Jess Enright will outline the depressing news that even if we exactly knew the network we needed to control and had perfect power to do so, it would still in the general case be computationally intractable to find the best-possible intervention. Luckily we are rarely in the general case! She will give a bit of hope by describing some of the special features of networks we see that help us build efficient algorithms to solve optimisation problems and give examples of these features in data-derived disease-relevant networks.
Speaker
is currently a Senior Lecturer in the School of Computing Science at the University of Glasgow. She completed a PhD at the University of Alberta in 2011, and then moved to Glasgow for a postdoc position as part of Scotland Centre of Expertise in Animal Disease Outbreaks working in network epidemiology. Her research includes work on graph algorithms, games on graphs and networks, complex networks, and modelling infectious diseases on networks.
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