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Alumno

Daniel Alejandro González Bandala

   »  Generación: Febrero2017
   »  Grado: Doctorado en Ciencias de la Comunicaci├│n
   »  Asesor: Juan Carlos Cuevas Tello
   »  Co-asesor: Dr. Christián Alberto García Sepúlveda
   »  Línea de Investigación: Sistemas Inteligentes
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NOMBRE DE TESIS
Computational forecasting of infectious disease dynamics

RESUMEN:

Infectious disease outbreaks have been a threat to human population for multiple reasons. The need to study the behavior of infectious diseases has been a scientific concern for many years, where being able to forecast outbreaks is thought to have great advantages for people, allowing health authorities to have an appropriate contingency plan applied in early stages of an outbreak. 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. Although, many disease forecasting techniques have been proposed, there is still a big field of research in this area, due to the many approaches that can be taken, the diverse variety of infectious diseases, the geographical restrictions, and many other factors. This research proposes to join multidisciplinary information and knowledge for surveillance and reliable early predictions of infectious disease outbreaks. The goal is to detect infectious disease outbreaks in early stages by using the MIDASmap tool as a georeferenced repository of: genetic bat data, up-to-date medical reports, and Internet search trend topics. This research deals with computational and bioinformatic techniques, and state-of-the-art forecasting methods.