Time-series analysis of tuberculosis from 2005 to 2017 in China.
Epidemiol Infect
; 146(8): 935-939, 2018 06.
Article
em En
| MEDLINE
| ID: mdl-29708082
ABSTRACT
Seasonal autoregressive integrated moving average (SARIMA) has been used to model nationwide tuberculosis (TB) incidence in other countries. This study aimed to characterise monthly TB notification rate in China. Monthly TB notification rate from 2005 to 2017 was used. Time-series analysis was based on a SARIMA model and a hybrid model of SARIMA-generalised regression neural network (GRNN) model. A decreasing trend (3.17% per years, P < 0.01) and seasonal variation of TB notification rate were found from 2005 to 2016 in China, with a predominant peak in spring. A SARIMA model of ARIMA (0,1,1) (0,1,1)12 was identified. The mean error rate of the single SARIMA model and the SARIMA-GRNN combination model was 6.07% and 2.56%, and the determination coefficient was 0.73 and 0.94, respectively. The better performance of the SARIMA-GRNN combination model was further confirmed with the forecasting dataset (2017). TB is a seasonal disease in China, with a predominant peak in spring, and the trend of TB decreased by 3.17% per year. The SARIMA-GRNN model was more effective than the widely used SARIMA model at predicting TB incidence.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Tuberculose
/
Modelos Teóricos
Tipo de estudo:
Diagnostic_studies
/
Incidence_studies
/
Prognostic_studies
Limite:
Humans
País/Região como assunto:
Asia
Idioma:
En
Revista:
Epidemiol Infect
Assunto da revista:
DOENCAS TRANSMISSIVEIS
/
EPIDEMIOLOGIA
Ano de publicação:
2018
Tipo de documento:
Article
País de afiliação:
China