Temporal and long-term trend analysis of class C notifiable diseases in China from 2009 to 2014. Issue 10 (17th October 2016)

• Record Type: Journal Article
• Authors: Zhang, Xingyu; Hou, Fengsu; Qiao, Zhijiao; Li, Xiaosong; Zhou, Lijun; Liu, Yuanyuan; Zhang, Tao
• Is Part Of: BMJ open. Volume 6:Issue 10(2016)
• Publication Date: 2016-10-17
• Subjects: infectious disease -- Seasonality -- long term trend -- time series; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2016-011038
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objectives: Time series models are effective tools for disease forecasting. This study aims to explore the time series behaviour of 11 notifiable diseases in China and to predict their incidence through effective models. Settings and participants: The Chinese Ministry of Health started to publish class C notifiable diseases in 2009. The monthly reported case time series of 11 infectious diseases from the surveillance system between 2009 and 2014 was collected. Methods: We performed a descriptive and a time series study using the surveillance data. Decomposition methods were used to explore (1) their seasonality expressed in the form of seasonal indices and (2) their long-term trend in the form of a linear regression model. Autoregressive integrated moving average (ARIMA) models have been established for each disease. Results: The number of cases and deaths caused by hand, foot and mouth disease ranks number 1 among the detected diseases. It occurred most often in May and July and increased, on average, by 0.14126/100 000 per month. The remaining incidence models show good fit except the influenza and hydatid disease models. Both the hydatid disease and influenza series become white noise after differencing, so no available ARIMA model can be fitted for these two diseases. Conclusion: Time series analysis of effective surveillance time series is useful for better understanding the occurrence of the 11 types of infectious disease.