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

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)424-429
Number of pages6
Issue number15
StatePublished - 2021
Event11th IFAC Symposium on Biological and Medical Systems BMS 2021 - Ghent, Belgium
Duration: Sep 19 2021Sep 22 2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering


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