In this talk, I present a mathematical model for the spread of an epidemic from one community to another via travel. Here each community is modeled by a random network (for simplicity, we assume it is an Erdos-Renyi random graph), with the epidemic spread inside the community given by the SIR model on this graph. Travel is modeled by individuals moving from one community to the other at some rate \eta_T, and returning home at another rate \eta_H. We assume that the return rate is of the same order as the recovery rate of the epidemic, while \eta_T is much smaller. Under this assumption, we rigorously prove that if an epidemic starts in the first community, and the second community enacts a travel ban at the moment the epidemic is large enough to be detectable, such a travel ban is ineffective in preventing a large outbreak in the second community. But contrast, other mitigation measure like social distancing or vaccinations (modeled by reducing the rate of infections in the second community) are effective.
This is joint work with my students Karissa Huang and Geng Zhao.
Bio: Christian Borgs is professor in the Berkeley AI Research Group (BAIR) in the EECS department at UC Berkeley, and faculty director of the Bakar Institute of Digital Materials for the Planet.
Borgs was trained as a physicist, holding a Ph.D. from the University of Munich and a Habilitation from the Free University in Berlin, and then becoming chair of Statistical Mechanics at the University of Leipzig. After that, he worked at Microsoft Research for about 20 years, first as co-founder and co-director of the Theory Group, and then as co-founder and Deputy Managing Director of Microsoft Research New England in Cambridge, MA, which was one of the first labs integrating research from areas as diverse as theoretical CS, ML, economics and qualitative social science. He joined Berkeley in 2020.
Borgs is a Fellow of the American Mathematical Society, and the American Association for the Advancement of Science. Among the earlier awards he has received are the Karl-Scheel Prize of the German Physical Society, and the Heisenberg Fellowship of the German Research Council.
Borgs’ current research focuses on both AI for science and the science of networks, including mathematical foundations, particularly the theory of graph limits aka Graphons (which he co-invented about 15 years ago), graph processes, graph algorithms, and applications of graph theory from economics to systems biology and epidemics. In addition, he has worked on various aspects of responsible AI, from differential privacy to questions of bias in automatic decision making or polarization in online communities. In AI for Science, he is interested in applications of AI to material science, with the aim to create materials to address urgent problems of society like climate change or scarcity of water in many regions of the world.
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