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Abstract: Contact patterns in populations fundamentally influence the spread ofinfectious diseases. Current mathematical methods for epidemiologicalforecasting on networks largely assume that contacts between individuals arefixed, at least for the duration of an outbreak. In reality, contact patternsmay be quite fluid, with individuals frequently making and breaking social orsexual relationships. Here we develop a mathematical approach to predictingdisease transmission on dynamic networks in which each individual has acharacteristic behavior (typical contact number), but the identities of theircontacts change in time. We show that dynamic contact patterns shapeepidemiological dynamics in ways that cannot be adequately captured in staticnetwork models or mass-action models. Our new model interpolates smoothlybetween static network models and mass-action models using a mixing parameter,thereby providing a bridge between disparate classes of epidemiological models.Using epidemiological and sexual contact data from an Atlanta high school, wethen demonstrate the utility of this method for forecasting and controllingsexually transmitted disease outbreaks.



Autor: Erik Volz, Lauren Ancel Meyers

Fuente: https://arxiv.org/







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