Examinando por Autor "M.E PUERTA"

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    Early Detection for Dengue Using Local Indicator of Spatial Association (LISA) Analysis.
    (MDPI, 2016-03-29) Parra, Mayra Elizabeth; M.E PUERTA; Lisarralde, Diana Paola; Arboleda, Sair; Parra, Mayra Elizabeth; M.E PUERTA; Lisarralde, Diana Paola; Arboleda, Sair; Universidad EAFIT. Departamento de Ciencias; Matemáticas y Aplicaciones
    Dengue is a viral disease caused by a flavivirus that is transmitted by mosquitoes of the genus Aedes. There is currently no specific treatment or commercial vaccine for its control and prevention; therefore, mosquito population control is the only alternative for preventing the occurrence of dengue. For this reason, entomological surveillance is recommended by World Health Organization (WHO) to measure dengue risk in endemic areas; however, several works have shown that the current methodology (aedic indices) is not sufficient for predicting dengue. In this work, we modified indices proposed for epidemic periods. The raw value of the epidemiological wave could be useful for detecting risk in epidemic periods; however, risk can only be detected if analyses incorporate the maximum epidemiological wave. Risk classification was performed according to Local Indicators of Spatial Association (LISA) methodology. The modified indices were analyzed using several hypothetical scenarios to evaluate their sensitivity. We found that modified indices could detect spatial and differential risks in epidemic and endemic years, which makes them a useful tool for the early detection of a dengue outbreak. In conclusion, the modified indices could predict risk at the spatio-temporal level in endemic years and could be incorporated in surveillance activities in endemic places.
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    REGRESIÓN POR MÍNIMOS CUADRADOS PARCIALES PLS CON DATOS DE INTERVALO
    (Universidad Nacional de Colombia, 2016-01-01) Gavria, Carlos Alberto; Pérez, Raul Alberto; M.E PUERTA; Gavria, Carlos Alberto; Pérez, Raul Alberto; M.E PUERTA; Universidad EAFIT. Departamento de Ciencias; Matemáticas y Aplicaciones
    La incertidumbre en los datos puede ser considerada mediante un intervalo numérico en el cual una variable puede asumir sus posibles valores, esto se conoce como datos de intervalo