COMPUTATIONAL RESEARCH in BOSTON and BEYOND (CRIBB)
| Date | February 3, 2017 |
|---|---|
| Speaker | Julian Kates-Harbeck Harvard University |
| Topic | Nonlinear Epidemics on Graphs |
| Abstract | Most epidemiological models assume independent transmission events, where the transmission probability between individuals is linear in the intensity/number of exposures. However, for social epidemics (such as behaviors, ideas, internet memes, opinions or other "virally" spreading social phenomena) it is not unreasonable to assume that there is a strong nonlinearity in the probability of an individual becoming infected as a function of the number of infected contacts. We study the behavior of such nonlinear epidemics on well-mixed populations as well as more realistic graphs. We provide analytical results which are compared to numerical simulations. |
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Acknowledgements
We thank the MIT Department of Mathematics, Student Chapter of SIAM, ORCD, and LLSC for their generous support of this series.