Comparison of predicting effect of schistosomiasis prevalence by 5 statistical models in the areas of "breaking dikes or opening sluice for water store" in Dongting Lake

    SAI Xiaoª²Yong, XING Qinª²Jun, MENG Dingª²Ru, JIA Yuª²Ran, CAI Kaiª²Ping, LI Yueª²Sheng, ZHOU Xiaoª²Nong

    1Department of Epidemiology, School of Preventive Medicine, Fourth Military Medical University, Xi¬ðan 710033, China

    2Department of Statistics, PLA 323 Hospital, Xi¬ðan 710054, China

    3Xi¬ðan First Cadre Sanatorium, General Logistics Department, Chinese PLA, Xi¬ðan 710054, China

    4Xiaozhai Cadre Sanatorium in Xi¬ðan, Lanzhou Military Area Command, Xi¬ðan 710061, China

    5Hunan Institute of Anti-epidemic of Schistosomiasis, Yueyang 414000, China

    6Institute of Parasitic Diseases, Chinese Center for Disease Prevention and Control, Shanghai 200025, China

    ¡¾Abstract¡¿ AIM: To compare the predicting effect of schistosomiasis prevalence by 5 different statistical models including Moving Average, Exponential Smoothing, Autoregressive Model, Autoregressive integrated moving average model (ARIMA Model) and Grey Model in the areas of "breaking dikes or opening sluice for water store" in Dongting Lake and to provide a fitted model for local schistosomiasis preventive department. METHODS: The 5 different statistical models were applied to predict the schistosomiasis prevalence in some experimental sites and Error Sum of Square (ESS), Average Relative Errors (ARE), Average Errors (AR) of 5 models were compared. RESULTS: ESS, ARE and AR of Grey Model in Jicheng were smallest; ESS and AR of ARIMA Model in Haohou were smallest; ARE of Autoregressive Model was smallest. CONCLUSION: Different models fit difª²ferent places. The predicting effects of Grey Model and ARIMA Model are best among the 5 models. ......