Time-series analysis of tuberculosis from 2005 to 2017 in China.

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. T

SUBMITTER: Wang H 

PROVIDER: S-EPMC9184947 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature