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Open Access Research article

Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model

Qiyong Liu12, Xiaodong Liu13, Baofa Jiang3 and Weizhong Yang4*

Author Affiliations

1 National Institute for Communicable Disease Control and Prevention, China CDC, Beijing, 102206, PR China

2 State Key Laboratory for Infectious Diseases Prevention and Control, Beijing, 102206, PR China

3 Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, 250012, Shandong Province, PR China

4 Chinese Center for Disease Control and Prevention, Beijing, 102206, PR China

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BMC Infectious Diseases 2011, 11:218  doi:10.1186/1471-2334-11-218

Published: 15 August 2011

Abstract

Background

China is a country that is most seriously affected by hemorrhagic fever with renal syndrome (HFRS) with 90% of HFRS cases reported globally. At present, HFRS is getting worse with increasing cases and natural foci in China. Therefore, there is an urgent need for monitoring and predicting HFRS incidence to make the control of HFRS more effective. In this study, we applied a stochastic autoregressive integrated moving average (ARIMA) model with the objective of monitoring and short-term forecasting HFRS incidence in China.

Methods

Chinese HFRS data from 1975 to 2008 were used to fit ARIMA model. Akaike Information Criterion (AIC) and Ljung-Box test were used to evaluate the constructed models. Subsequently, the fitted ARIMA model was applied to obtain the fitted HFRS incidence from 1978 to 2008 and contrast with corresponding observed values. To assess the validity of the proposed model, the mean absolute percentage error (MAPE) between the observed and fitted HFRS incidence (1978-2008) was calculated. Finally, the fitted ARIMA model was used to forecast the incidence of HFRS of the years 2009 to 2011. All analyses were performed using SAS9.1 with a significant level of p < 0.05.

Results

The goodness-of-fit test of the optimum ARIMA (0,3,1) model showed non-significant autocorrelations in the residuals of the model (Ljung-Box Q statistic = 5.95,P = 0.3113). The fitted values made by ARIMA (0,3,1) model for years 1978-2008 closely followed the observed values for the same years, with a mean absolute percentage error (MAPE) of 12.20%. The forecast values from 2009 to 2011 were 0.69, 0.86, and 1.21per 100,000 population, respectively.

Conclusion

ARIMA models applied to historical HFRS incidence data are an important tool for HFRS surveillance in China. This study shows that accurate forecasting of the HFRS incidence is possible using an ARIMA model. If predicted values from this study are accurate, China can expect a rise in HFRS incidence.