B1742
Title: Uncertainty analysis of contagion processes based on a functional depth approach
Authors: Dunia Lopez-Pintado - University Pablo de Olavide (Spain)
Sara Lopez Pintado - Northeastern University (United States) [presenting]
Ivan Garcia Milan - Universidad de Loyola (Spain)
Zonghui Yao - Northeastern University (United States)
Abstract: The spread of a disease, product or idea in a population is often hard to predict. One or few realizations of the contagion process are observed and therefore limited information can be obtained for anticipating future similar events. The stochastic nature of contagion generates unpredictable outcomes throughout the whole course of the dynamics. This might lead to important inaccuracies in the predictions and to the over or under-reaction of policymakers, who tend to anticipate the average behaviour. Through an extensive simulation study, the properties of the contagion process are analyzed, focusing on its unpredictability or uncertainty, and exploiting the functional nature of the data. In particular, a novel non-parametric measure of variance is defined based on weighted depth-based central regions. This methodology is applied to the susceptible-infected-susceptible epidemiological model and small-world networks. It is found that maximum uncertainty is attained at the epidemic threshold. The density of the network and the contagiousness of the process have a strong and complementary effect on the uncertainty of contagion, whereas only a mild effect of the network's randomness structure is observed.
