deletions | additions       diff --git a/section_Introduction_The_Chagas_disease__.tex b/section_Introduction_The_Chagas_disease__.tex  index f399b58..f136301 100644  --- a/section_Introduction_The_Chagas_disease__.tex  +++ b/section_Introduction_The_Chagas_disease__.tex 
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% \begin{comment} en el drive estan las ppt del min salud \end{comment}.   The spatial dissemination of a congenitally transmitted disease sidesteps the available measures to control risk groups, and shows that individuals who have not been exposed to the disease vector should also be included in detection campaigns.  Working in collaboration with the \textit{Mundo Sano} Foundation, who provided key health expertise on the subject, in In  this work we discuss the use of Call Detail Records (CDRs) for the analysis of mobility patterns and the detection of possible risk zones of Chagas disease in two Latin America countries. This project was performed in collaboration with the \textit{Mundo Sano} Foundation, who provided key health expertise on the subject.  We generate predictions of population movements between different regions, providing a proxy for the epidemic spread. Our objective is to show that geolocalized call records are rich in social and individual information, which can be used to determine whether an individual has lived in an epidemic area. We present two case studies, in Argentina and in Mexico, using data provided by mobile phone companies from each country. A discussion of how mobile data was processed is included. Mobile phone records contain information about the movements of large subsets of the population of a country, and make them very useful to understand the spreading dynamics of infectious diseases. They have been used to understand the diffusion of malaria in Kenya~\cite{wesolowski2012quantifying} and in Ivory Coast~\cite{enns2013human}, including the refining of infection models~\cite{chunara2013large}.   The referenced works on Ivory Coast were performed using the D4D (Data for Development) challenge datasets released in 2013. Additional studies based on the Ivory Coast dataset are reviewed in \cite{naboulsi2015mobile}.