Application Of Three Model In Forecasting Incidence Of The Main Communicable Diseases

BackgroundsCommunicable diseases have the characteristics of communicability which are caused by pathogenic microorganism and parasites, they are also diseases which seriously impair human health.In recent years the emergence of new infectious diseases, such as severe acute respiratory syndrome and human infection with highly pathogenic avian influenza, was a sever challenge for the prevention and control of human infectious diseases in the 21st century. From 1980s, the theory and application of prediction of infectious diseases have been rapidly developed and the works have become a hot spot in terms of disease surveillance. Prediction of disease can show the development trend of the disease, which carry out the basis for early warning of disease and provide a theoretical basis for the development of prevention strategies and measures. Therefore, it is necessary to explore the epidemiology of the main communicable diseases. By comparing different models which used to forecast the incidence of main communicable diseases, select the appropriate forecasting model to predict trends and evaluate results of prevention and control measures. Materials & MethodsThe study based on the incidence of 17 infectious diseases in Jiashan County between 1951 to 2009,include:①analyzing the epidemic trend of 17 infectious diseases,the epidemic trend and seasonal characteristics of three main infectious diseases(viral hepatitis, dysentery and measles) in Jiashan County using descriptive epidemiological methods;②comparing exponential curve, gray GM (1,1) and ARIMA model which were used to forecast three main infectious diseases by mean error rate(MER), R2 and the residuals of point prediction; Select the appropriate forecasting model to predict trends of main infectious diseases.Results1 General situation of the incidence rate of 17 kinds of legal infections from 1951 to 2009 in Jiashan CountyThe average incidence rate of 17 kinds notifiable infectious diseases was high from mid-1950s to the first half of the 1960s, which remained stable in 1970s and 1980s, but decreased dramatically from early 1990s. After that the total incidence of notifiable infectious diseases stayed constant at lower level. The average incidence rate dropped from 4938.73/lakh(1960s) to 90.27/lak(2000s).The spectrums of top five infectious changed in different years, in 1950s, the infectious were measles, dysentery, whooping cough, malaria and epidemic meningitis, but in 2000s,the spectrums of infection were viral hepatitis, dysentery, measles, typhoid and malaria, but the incidence rate of typhoid and malaria was only 4.36/lakh and 0.35/lakh. Therefore, viral hepatitis, dysentery and measles were the main communicable diseases in Jiashan County because of the incidence of the top three.2 Application of three model in forecasting incidence of viral hepatitisThere was a continuous downtrend in the incidence of Hepatitis A which decreased from 214.13/lakh in 1990 to 0.15/lakh in 2009. There was a fluctuation in the incidence of Hepatitis B between 47.74/lakh in 1990 and 56.97/lakh in 2003; after that, the incidence of Hepatitis B decreased to the lowest level in 2009(16.56/lakh).There was a seasonal variation in the incidence of Hepatitis A, the incidence rates in winter and spring was higher than in other seasons. There was no distinct seasonal variation in the incidence of Hepatitis B but it was slightly higher in January than in other months.Three model were used to forecast the incidence of Hepatitis B. GM (1,1) model could not be used to predict the incidence of Hepatitis B, exponential curve model and the ARIMA model could be used to predict it. The MER of exponential curve model and ARIMA (0,1,1)×(0,1,1)4 model was 16.40% and 10.10%, and the R2 was 0.21 and 0.71 respectively. The predicted incidence of Hepatitis B in 2009 by two models was 28.13/lakh and 20.16/lakh respectively. According to the actual incidence rate in 2009(16.56/lakh) the point prediction residual was 11.57/lakh and 3.60/lakh respectively. Using the best model ARIMA (0,1,1)×(0,1,1) 4 model to predict the incidence rate of Hepatitis B,it would be 15.30/lakh and 13.34//lakh in 2010 and 2011 respectively.3 Application of three model in forecasting incidence of dysenteryThe average incidence of dysentery was higher in 1950s(224.40/lakh) than 1960s(110.55/lakh),was highest from 1970s to 1980s(more than 500/lakh). There was a continuous downtrend from late 1980s in the incidence of dysentery which decreased to the lowest level in 2000s(20.56/lakh). There was a seasonal variation in the incidence of dysentery in different years, the incidence rates in summer and autumn was higher than in other months.Three models were used to forecast the incidence of dysentery. Exponential curve, GM (1,1) and the ARIMA (0,1,1)×(0,1,1)4model all could be used to predict the incidence of dysentery. The MER of three models was 44.21%,28.00% and 19.87%, the R2 was 0.76,0.94 and 0.93 respectively. The predicted incidence of dysentery in 2009 by three models was 8.09/lakh,1.45/lakh and 5.93/lakh respectively. According to the actual incidence rate in 2009(3.92/lakh) the point prediction residual was 4.17/lakh,2.47/lakh and 2.01/lakh respectively. Using the best model ARIMA (0,1,1)×(0,1,1)4 model to predict the incidence rate of dysentery, it would be 1.67/lakh and 0.98//lakh in 2010 and 2011 respectively.4 Application of three model in forecasting incidence of measlesIn the different periods when measles vaccine was not used (1951 to 1965), small-scale used (1966 to 1969), annual vaccination (1970 to 1983) and mid-month vaccination (1984 to 2009), the average incidence of measles was 871.10/lakh, 264.76/lakh,80.54/lakh and 8.82/lakh respectively. There was some seasonal in the incidence of measles in four periods.Three models were used to forecast the incidence of measles. Exponential curve and GM (1,1) model could not be used to predict it, the ARIMA (1,1,0) model could be used to predict it in theory. The MER of ARIMA(1,1,0) model was 75.03% and the R2 was 0.09. The predicted incidence of measles in 2009 was 13.61/lakh. According to the actual incidence rate in 2009(4.21/lakh), the point prediction residual was 9.40/lakh and the relative error was 223.28%. The ARIMA (1,1,0) model which had poor fitting and poor accuracy could not be used to predict the incidence of measles.Conclusion1 Exponential curve model could well predict the incidence of dysentery which showed a continuous downtrend and exponential change.Prediction result was not satisfactory for Hepatitis B which showed a downward trend after a slight fluctuation.It was not used to predict the incidence of measles which had large fluctuations.2 GM(1,1) model could well predict the incidence of dysentery which showed a continuous downtrend and exponential change.It was not used to predict the incidence of Hepatitis B which showed a downward trend after a slight fluctuation and the incidence of measles which had large fluctuations.3 ARIMA model could well predict the incidence of Hepatitis B which showed a downward trend after a slight fluctuation and the incidence of dysentery which showed a continuous downtrend and exponential change. It was not used to predict the incidence of measles which had large fluctuations and a small amount of data. In common models of one-dimensional time series for the incidence of infectious diseases, ARIMA model had good fitting and accuracy. Predicted results for the incidence of Hepatitis B showed a slow decline and for the incidence of dysentery showed a decline at lower level.4 Although the prediction model could predict the trend of infectious diseases, but the application also had some limitations.Exponential curve model and GM (1,1) model was not suitable to forecast the infectious diseases which had large fluctuations. ARIMA model was not suitable to predict the infectious diseases which had large fluctuations and a small amount of data.