Article Text
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.
- infectious disease
- Seasonality
- long term trend
- time series
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Footnotes
Contributors XZ, TZ, LZ and FH conceived and designed the experiments. XZ and TZ collected the data and performed the statistical analysis. XZ, FH, ZQ, XL, LZ, YL and TZ participated in drafting the manuscript including data analysis and interpretation of results. All authors read and approved the final manuscript.
Funding TZ was supported by Sichuan University grant ‘the Fundamental Research Funds for the Central Universities’ (grant number 2016SCU11006) and the National Natural Science Foundation of China (grant no.81602935). The research is funded by the National Science and Technology Major Special Project ‘Data mining and analysis of the surveillance data of five syndrome pathogens (grant number 2012ZX10004201-006)’. XZ was supported financially by the China Scholarship Council for his doctoral studies.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement The dataset is available from the corresponding author at scdxzhangtao@163.com.