Contagion Models and Adaptive Behavior
We hypothesize that closing the epidemic-information-behavior feedback loop will significantly change the dynamics and predictions of realistic computational models for epidemics. In order to test this hypothesis we aim at developing a new class of computational modeling frameworks for human disease dynamics that account for social adaptive behavior induced by information spreading and other awareness processes. These models will be based on effective coupling parameters defining social distancing and other risk perception phenomena. In order to feed these models with plausible parameters we also tackle the analysis of how and to what extent information on the state and time course of an epidemic affects inter-individual contacts and human mobility by analyzing longitudinal series of pervasive data collected on mobile communication databases (smart phones, twitter logs, facebook, etc.) on human social interactions and individual mobility patterns.
We hypothesize that closing the epidemic-information-behavior feedback loop will significantly change the dynamics and predictions of realistic computational models for epidemics. In order to test this hypothesis we aim at developing a new class of computational modeling frameworks for human disease dynamics that account for social adaptive behavior induced by information spreading and other awareness processes. These models will be based on effective coupling parameters defining social distancing and other risk perception phenomena. In order to feed these models with plausible parameters we also tackle the analysis of how and to what extent information on the state and time course of an epidemic affects inter-individual contacts and human mobility by analyzing longitudinal series of pervasive data collected on mobile communication databases (smart phones, twitter logs, facebook, etc.) on human social interactions and individual mobility patterns.
