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Center for Nonlinear Studies |
One of the most important goals of infectious disease epidemiology is to design efficient interventions to prevent or contain epidemics. In >> a network-based Susceptible-Infectious-Removed" (SIR) epidemic model, infection is transmitted across edges between nodes in a network called the ``contact network". The most effective vaccination >> strategy for these models is to target nodes with the highest degree(number of neighbors) in the contact network. But can we really have >> the same optimal vaccination strategy for all diseases spreading on the same network? In this talk, we present an alternative strategy >> based on a mapping from a stochastic SIR model to a directed random network that we call the ``epidemic percolation network" (EPN). Above >> the epidemic threshold, the EPN contains a ``giant strongly-connected component" (GSCC), a unique largest group of nodes in which every node >> can be reached from every other node by following a series of edges. We show that targeting nodes in the GSCC reduces both the probability >> and final size of an epidemic more rapidly than targeting high-degree >> nodes in the contact network, particularly in models with substantial >> heterogeneity in infectiousness and susceptibility. Another important >> advantage of our approach is that it applies to all time-homogeneous >> SIR models, including fully-mixed models (which still dominate >> infectious disease research). The concept of the EPN gives us a >> unified theoretical framework for SIR epidemic models that may have >> extremely important practical applications. >> >>
Host: Joel Miller