Predictive Efficiency Comparison of ARIMA-Time-Series and the Grey System GM(1,1) Forecast Model on Forecasting the Incidence Rate of Hepatitis B

Prediction of reported monthly incidence of hepatitis B in Hainan Province of China based on SARIMA-BPNN model

In recent years, the incidence of hepatitis B has been serious in Hainan Province of China. To construct a statistical model of the monthly incidence of hepatitis B in Hainan Province of China and predict the monthly incidence of hepatitis B in 2022. Simple central moving average method and seasonal index were used to analyze the trend and seasonal effects of monthly incidence of hepatitis B. Based on the time series of reported monthly incidence of hepatitis B in Hainan Province from 2017 to 2020, a multiplicative seasonal model (SARIMA), multiplicative seasonal model combined with error back propagation neural network model (SARIMA-BPNN), and a gray prediction model were constructed to fit the incidence, and the time series of monthly incidence of hepatitis B in 2021 was used to verify the accuracy of models. The lowest and highest monthly incidence of hepatitis B in Hainan Province were in February and August, respectively, and MAPE of SARIMA, SARIMA-BPNN, and gray prediction models were 0.089, 0.087, and 0.316, respectively. The best fitting model is the SARIMA-BPNN model. The predicted monthly incidence of hepatitis B in 2022 showed a downward trend, with the steepest decline in March, which indicates that the prevention and control of hepatitis B in Hainan Province is effective, and the study can provide scientific and reasonable suggestions for the prevention and control of hepatitis B in Hainan.

Time-series analysis on human brucellosis during 2004-2013 in Shandong Province, China

Human brucellosis is a re-emerging bacterial anthropozoonotic disease, which remains a public health concern in China with the growing number of cases and more widespread natural foci. The purpose of this study was to short-term forecast the incidence of human brucellosis with a prediction model. We collected the annual and monthly laboratory data of confirmed cases from January 2004 to December 2013 in Shandong Diseases Reporting Information System (SDRIS). Autoregressive integrated moving average (ARIMA) model was fitted based on the monthly human brucellosis incidence from 2004 to 2013. Finally, monthly brucellosis incidences in 2014 were short-term forecasted by the obtained model. The incidence of brucellosis was increasing from 2004 to 2013. For the ARIMA (0, 2, 1) model, the white noise diagnostic check (x(2) = 5.58 P = 0.35) for residuals obtained was revealed by the optimum goodness-of-fit test. The monthly incidences that fitted by ARIMA (0, 2, 1) model were closely consistent with the real incidence from 2004 to 2013. And forecasting incidences from January 2014 to December 2014 were, respectively, 0.101, 0.118, 0.143, 0.166, 0.160, 0.172, 0.169, 0.133, 0.122, 0.105, 0.103 and 0.079 per100 000 population, with standard error 0.011-0.019 and mean absolute percentage error (MAPE) of 58.79%.