Robust kalman filter for tuberculosis incidence time series forecasting Documento de conferencia

Thesis author

Andrés Leonardo Jutinico
Carlos Enrique Awad Garciá
Erika Paola Vergara Vela
Maria Angélica Palencia

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.

autores

http://vivoweb.org/ontology/core#FacultyMember
Orjuela Cañón, Alvaro David

fecha de publicación

2021-1-1

Área temática

Salud y bienestar

Edición

54

Número Internacional Normalizado de Publicaciones Seriadas (ISSN)

2405-8963

Identificador de objeto digital (DOI)

https://doi.org/10.1016/j.ifacol.2021.10.293

Número de páginas

6

Página inicial

424

Última página

429