[Grey model prediction of occupational diseases in Changsha].

Objective: To understand and predict the situation of occupational diseases in Changsha, and to provide theoretical basis for the scientific formulation of occupational diseases prevention, control strategies and measures. Methods: In April 2019, the data of occupational diseases incidences from 2010 to 2018 were collected. The original GM (1, 1) grey model and buffer operator improved model were established, and compared their prediction effect. The model with the smallest average relative error was selected to predict the incidence of occupational diseases during 2019-2023. Results: The relative accuracy of the original GM (1, 1) grey model and the first-order and second-order buffer operator improved model were 80.92%, 97.71%, 99.64%, respectively. And the c values were 0.74, 0.28, 0.09, and the P values were 0.67, 1.00, 1.00, respectively. It was predicted that the incidence number of occupational diseases in Changsha during 2019-2023 would be 40, 39, 39, 38, 37, respectively. Conclusion: The buffer operator improved model is suitable for the prediction of the original data series with high volatility, and it can fit the incidence of occupational diseases in Changsha.

[The application of ARIMA in forecasting the cases of rabies in China different human groups].

Objective: To understand the situation of rabies in China different human groups. Seasonal ARIMA model is established to make forecasts and to provide suggestions for human rabies controlling in China. Methods: Build a model with data during 2004-2013, and validate the model by data of 2014. Then predict the cases of rabies from 2015-2017. Results: The long term trend is decreasing year by year, while seasonal effect is found that number of the third and fourth quarter are higher than others. The seasonal ARIMA model was built, whose residual are white noise. The RMAE in forecasting of peasants, students and scattered children are 19.10%、49.93% and 68.01%. Conclusion: The decreasing of rabies cases in China with time shows that the measures for control are effective. October for peasants, September for students, August for scattered children are critical period in future for rabies' control. Seasonal ARIMA model is a feasible model in forecasting the cases of rabies in China different groups in some way, error will be reduced by modeling separately for different wave sequence and combining with other models like ARIMA-GARCH.