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

doi:10.1016/j.ifacol.2021.10.293

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

Producción científica: Contribución a revista › Artículo de la conferencia › revisión exhaustiva

4 Citas (Scopus)

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 original	Inglés estadounidense
Páginas (desde-hasta)	424-429
Número de páginas	6
Publicación	IFAC-PapersOnLine
Volumen	54
N.º	15
DOI	https://doi.org/10.1016/j.ifacol.2021.10.293
Estado	Publicada - 2021
Evento	11th IFAC Symposium on Biological and Medical Systems BMS 2021 - Ghent, Bélgica Duración: sept 19 2021 → sept 22 2021

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 3: Salud y bienestar

Áreas temáticas de ASJC Scopus

Ingeniería de control y sistemas

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10.1016/j.ifacol.2021.10.293

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@article{d7912dfa361b470abf1b06ff12335b49,

title = "Robust kalman filter for tuberculosis incidence time series forecasting",

abstract = "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.",

author = "Jutinico, \{Andres L.\} and Erika Vergara and Garci{\'a}, \{Carlos Enrique Awad\} and Palencia, \{Maria Ang{\'e}lica\} and Orjuela-Ca{\~n}on, \{Alvaro David\}",

note = "Publisher Copyright: {\textcopyright} 2021 The Authors.; 11th IFAC Symposium on Biological and Medical Systems BMS 2021 ; Conference date: 19-09-2021 Through 22-09-2021",

year = "2021",

doi = "10.1016/j.ifacol.2021.10.293",

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T1 - Robust kalman filter for tuberculosis incidence time series forecasting

AU - Jutinico, Andres L.

AU - Vergara, Erika

AU - Garciá, Carlos Enrique Awad

AU - Palencia, Maria Angélica

AU - Orjuela-Cañon, Alvaro David

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85120674178

UR - https://www.scopus.com/pages/publications/85120674178#tab=citedBy

U2 - 10.1016/j.ifacol.2021.10.293

DO - 10.1016/j.ifacol.2021.10.293

M3 - Conference article

AN - SCOPUS:85120674178

SN - 2405-8963

VL - 54

SP - 424

EP - 429

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 15

T2 - 11th IFAC Symposium on Biological and Medical Systems BMS 2021

Y2 - 19 September 2021 through 22 September 2021

ER -

Robust kalman filter for tuberculosis incidence time series forecasting

Resumen

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