A Bayesian Approach to Income Inference in a Communication Network

The explosion of mobile phone communications in the last years occurs at a moment where data processing power increases exponentially. Thanks to those two changes in a global scale, the road has been opened to use mobile phone communications to generate inferences and characterizations of mobile phone users. In this work, we use the communication network, enriched by a set of users' attributes, to gain a better understanding of the demographic features of a population. Namely, we use call detail records and banking information to infer the income of each person in the graph.

Uncovering the Spread of Chagas Disease in Argentina and Mexico

Chagas disease is a neglected disease, and information about its geographical spread is very scarse. We analyze here mobility and calling patterns in order to identify potential risk zones for the disease, by using public health information and mobile phone records. Geolocalized call records are rich in social and mobility information, which can be used to infer whether an individual has lived in an endemic area. We present two case studies in Latin American countries. Our objective is to generate risk maps which can be used by public health campaign managers to prioritize detection campaigns and target specific areas. Finally, we analyze the value of mobile phone data to infer long-term migrations, which play a crucial role in the geographical spread of Chagas disease.