Robust kalman filter for tuberculosis incidence time series forecasting

Andres L. Jutinico, Erika Vergara, Carlos Enrique Awad Garciá, Maria Angélica Palencia, Alvaro David Orjuela-Cañon

Resultado de la investigación: Contribución a una revistaArtículo de la conferenciarevisión exhaustiva

Resumen

Governments must detect and treat people with tuberculosis, also prevent the uninfected community. In this sense, must promote the study of algorithms for the prediction of the epidemic trend. This paper addresses the forecasting of tuberculosis cases in Bogota, considering health surveillance system data from 2007-2020. Forecasts are obtained using the Kalman Filter and the Robust Kalman Filter. Results show better performance using the robust filter for six-week tuberculosis cases prediction.

Idioma originalInglés estadounidense
Páginas (desde-hasta)424-429
Número de páginas6
PublicaciónIFAC-PapersOnLine
Volumen54
N.º15
DOI
EstadoPublicada - 2021
Evento11th IFAC Symposium on Biological and Medical Systems BMS 2021 - Ghent, Bélgica
Duración: sept. 19 2021sept. 22 2021

Áreas temáticas de ASJC Scopus

  • Ingeniería de control y sistemas

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