Daniel Alejandro González Bandala

   »  Generación: Febrero2017
   »  Grado: Doctorado en Ciencias de la Comunicaci├│n
   »  Línea de Investigación: Sistemas Inteligentes
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Computational forecasting of infectious disease dynamics


Recently, there have been many zoonotic viral disease outbreaks. Usually, health authorities have limited their actions to react to already advanced outbreaks. This reactive behavior is expensive because it requires greater infrastructure, human resources, and has many casualties. Bats act as important reservoir hosts for many of these emerging viruses, as they represent one fifth of the whole mammal population known. While preventive measures are taken many intermediate hosts and human populations might be at the brink of a disease outbreak. This research proposal focuses on using computational and bioinformatic techniques, a system called MIDASmap as a repository of genetic bat data mixed with georeferenced data, medical reports and Internet search trend topics, to find patterns pointing to potential emerging infectious diseases, as part of a wider multidisciplinary project in an effort to prevent unmonitored zoonotic viral disease outbreaks. Keywords: bioinformatics, pattern recognition, data mining, neural networks, bats, zoonosis, forecast