TY - JOUR
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
N1 - Publisher Copyright:
© 2021 The Authors.
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 - http://www.scopus.com/inward/record.url?scp=85120674178&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85120674178&partnerID=8YFLogxK
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 -
