RESUMEN
Human mobility and social structure are at the basis of disease spreading. Disease containment strategies are usually devised from coarse-grained assumptions about human mobility. Cellular networks data, however, provides finer-grained information, not only about how people move, but also about how they communicate. In this paper we analyze the behavior of a large number of individuals in Ivory Coast using cellular network data. We model mobility and communication between individuals by means of an interconnected multiplex structure where each node represents the population in a geographic area (i.e., a sous-préfecture, a third-level administrative region). We present a model that describes how diseases circulate around the country as people move between regions. We extend the model with a concurrent process of relevant information spreading. This process corresponds to people disseminating disease prevention information, e.g., hygiene practices, vaccination campaign notices and other, within their social network. Thus, this process interferes with the epidemic. We then evaluate how restricting the mobility or using preventive information spreading process affects the epidemic. We find that restricting mobility does not delay the occurrence of an endemic state and that an information campaign might be an effective countermeasure.
Asunto(s)
Control de Enfermedades Transmisibles , Difusión de la Información , Modelos Teóricos , Movilidad Social , Algoritmos , Control de Enfermedades Transmisibles/métodos , Enfermedades Transmisibles/epidemiología , Simulación por Computador , Epidemias , HumanosRESUMEN
The announcement of the discovery of a Higgs boson-like particle at CERN will be remembered as one of the milestones of the scientific endeavor of the 21(st) century. In this paper we present a study of information spreading processes on Twitter before, during and after the announcement of the discovery of a new particle with the features of the elusive Higgs boson on 4(th) July 2012. We report evidence for non-trivial spatio-temporal patterns in user activities at individual and global level, such as tweeting, re-tweeting and replying to existing tweets. We provide a possible explanation for the observed time-varying dynamics of user activities during the spreading of this scientific "rumor". We model the information spreading in the corresponding network of individuals who posted a tweet related to the Higgs boson discovery. Finally, we show that we are able to reproduce the global behavior of about 500,000 individuals with remarkable accuracy.
Asunto(s)
Difusión de la Información , Modelos Teóricos , Medios de Comunicación Sociales , Algoritmos , Simulación por Computador , Humanos , Análisis Espacio-Temporal , Factores de TiempoRESUMEN
Connections in complex networks are inherently fluctuating over time and exhibit more dimensionality than analysis based on standard static graph measures can capture. Here, we introduce the concepts of temporal paths and distance in time-varying graphs. We define as temporal small world a time-varying graph in which the links are highly clustered in time, yet the nodes are at small average temporal distances. We explore the small-world behavior in synthetic time-varying networks of mobile agents and in real social and biological time-varying systems.