Motores Marinos

To rectify the imprecision of existing techniques for mathematically modeling epi- demics, we have simulated disease spreading on a high resolution social network, using a real-world dataset that captures individual human interactions with extremely fine granularity. Using these simulations, we implemented twelve strategies for intervening and either preventing or containing an infection. We showed that, by representing the network as a graph, we could apply existing graph metrics to inform more intelligent interventions. Notably, we showed that targeted vaccination based on the degree distribution or the density decomposition of the network could delay or prevent a measles-like epidemic using fewer vaccinations than existing estimates, which must assume a random distribution.

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