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Center for Nonlinear Studies |
In the early 20th century, two epidemiologists introduced a simple and powerful deterministic model for predicting infectious disease transmission which tracks the unidirectional movement of hosts among three states ? susceptible (S), infected (I), and recovered (R). This SIR model provides important insight into the temporal progression of outbreaks and the efficacy of vaccination, and is the foundation for a recent proliferation in predictive methods. Contact network epidemiology is a particularly promising development in which bond percolation on random graphs is applied to modeling disease transmission through heterogeneous populations. My lecture will introduce the SIR model, explain its generalization to disease propagation on graphs in which vertices and edges represent individual hosts and disease-causing contacts, respectively, and link recent theoretical results to issues of public health. In particular, I will address the question of how best to distribute a limited supply of influenza vaccines to reduce the burden of morbidity and mortality caused by the influenza virus.