Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network.
Epidemiol Infect
; 145(6): 1118-1129, 2017 04.
Article
in En
| MEDLINE
| ID: mdl-28115032
ABSTRACT
Tuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Tuberculosis
/
Epidemiologic Methods
/
Neural Networks, Computer
Type of study:
Incidence_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Country/Region as subject:
Asia
Language:
En
Journal:
Epidemiol Infect
Journal subject:
DOENCAS TRANSMISSIVEIS
/
EPIDEMIOLOGIA
Year:
2017
Document type:
Article