Time series forecasting for tuberculosis incidence employing neural network models

Orjuela-Cañón, Álvaro David; Jutinico, Andrés L.; Duarte-González, Mario Enrique; García, Carlos Enrique Awad; Vergara, Erika; Palencia, María Angélica

doi:10.1016/j.heliyon.2022.e09897

Cited by 6 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In state-of-the-art, several forecasting methods exist for infectious diseases, for instance [5]. However, few studies deal with TB prediction in Colombia [6], [7]. Also, TB forecasting is still a challenge given the abrupt changes in trend and dispersion of TB incidence relative to each region or country.…”

Section: Introductionmentioning

confidence: 99%

Towards Tuberculosis Incidence Time Series Forecasting in Colombian Regions: The Antioquia Case

Sanchez-Vega¹,

Orjuela-Cañón²,

Garcia³

et al. 2022

Machine Learning Techniques and Data Science Trends

View full text Add to dashboard Cite

Antioquia is a Colombian department where 6.7 million people live. Currently, it is the region of the country with the newest cases of tuberculosis reported in 2021, about 18.8%. In addition, the incidence rate of tuberculosis was 36.8 per 100,000 inhabitants. Public government health policy regarding tuberculosis should aim to prevent the uninfected community, in addition to detecting and treating people with tuberculosis. In this sense, the study of algorithms to predict the epidemic trend should be promoted. This work addresses the prediction of tuberculosis cases in Antioquia, considering data from the health surveillance system between 2007 and 2021. For the prediction, the Kalman filter and the autoregressive model are considered. The results show a better performance using the Kalman filter for the prediction of tuberculosis cases at six weeks.

show abstract

Section: Introductionmentioning

confidence: 99%