Time series analysis-based seasonal autoregressive fractionally integrated moving average to estimate hepatitis B and C epidemics in China

BACKGROUND Hepatitis B(HB)and hepatitis C(HC)place the largest burden in China,and a goal of eliminating them as a major public health threat by 2030 has been set.Making more informed and accurate forecasts of their spread is essential for developing effective strategies,heightening the requirement for early warning to deal with such a major public health threat.AIM To monitor HB and HC epidemics by the design of a paradigmatic seasonal autoregressive fractionally integrated moving average(SARFIMA)for projections into 2030,and to compare the effectiveness with the seasonal autoregressive integrated moving average(SARIMA).METHODS Monthly HB and HC incidence cases in China were obtained from January 2004 to June 2023.Descriptive analysis and the Hodrick-Prescott method were employed to identify trends and seasonality.Two periods(from January 2004 to June 2022 and from January 2004 to December 2015,respectively)were used as the training sets to develop both models,while the remaining periods served as the test sets to evaluate the forecasting accuracy.RESULTS There were incidents of 23400874 HB cases and 3590867 HC cases from January 2004 to June 2023.Overall,HB remained steady[average annual percentage change(AAPC)=0.44,95%confidence interval(95%CI):-0.94-1.84]while HC was increasing(AAPC=8.91,95%CI:6.98-10.88),and both had a peak in March and a trough in February.In the 12-step-ahead HB forecast,the mean absolute deviation(15211.94),root mean square error(18762.94),mean absolute percentage error(0.17),mean error rate(0.15),and root mean square percentage error(0.25)under the best SARFIMA(3,0,0)(0,0.449,2)12 were smaller than those under the best SARIMA(3,0,0)(0,1,2)12(16867.71,20775.12,0.19,0.17,and 0.27,respectively).Similar results were also observed for the 90-step-ahead HB,12-step-ahead HC,and 90-step-ahead HC forecasts.The predicted HB incidents totaled 9865400(95%CI:7508093-12222709)cases and HC totaled 1659485(95%CI:856681-2462290)cases during 2023-2030.CONCLUSION Under current interventions,China faces enormous challenges to eliminate HB and HC epidemics by 2030,and effective strategies must be reinforced.The integration of SARFIMA into public health for the management of HB and HC epidemics can potentially result in more informed and efficient interventions,surpassing the capabilities of SARIMA.

天气衍生品气温预测模型对比研究

气温衍生品是天气衍生品交易中最活跃的合约之一,确定合理预测气温动态变化的模型,是气温衍生品开发设计的基础。考虑到气温在时间变化上具有趋势性、季节性和周期性等特点,文中使用了以O-U均值回复过程为基础的Continuous Time Autoregressive Model(CAR)模型、Seasonal Autoregressive Integrated Moving Average(SARIMA)模型和小波神经网络算法,并选择漠河、北京、乌鲁木齐、芜湖、昆明和海口具有地域性代表的城市气温进行拟合,使用无偏绝对百分比误差、绝对百分比误差和平均绝对比例误差检验指标检验了模型的预测精度。研究结果表明,小波神经网络算法在预测6个城市的无偏绝对百分比误差、绝对百分比误差和平均绝对比例误差的值最小;同时,相比CAR模型、SARIMA模型,其预测效果最优。因此,小波神经网络算法能够很好地拟合气温数据的变化,可以为我国气温天气衍生品的定价提供一定的指导。

Forecasting the Monthly Reported Cases of Human Immunodeficiency Virus (HIV) at Minna Niger State, Nigeria

There has been a moderate increase in newly diagnosed HIV-infected Minna populace, which calls for serious attention. This study used time series data based on monthly HIV cases from January 2007 to December 2018 taken from the statistical data document on HIV prevalence recorded in General Hospital Minna, Niger State. The methodology employed to analyze the data is based on mathematical models of ARMA, ARIMA and SARIMA which were computed and diagnosed. From the results of parameter estimation of the models, ARMA(2, 1) model was the best model among the other ARMA models using information criteria (AIC). Diagnostic test was run on the ARMA(2, 1) model where the results show that the model was adequate and normally distributed using Box-Lung test and Q-Q plot respectively. Furthermore, ARIMA of first and second differences was estimated and ARIMA(1, 0, 1) was the best model from the result of the AIC and diagnostic test carried out which revealed that the model was adequate and normally distributed using Box-Lung and Q-Q plot respectively. Furthermore, the results obtained in the ARMA and ARIMA models were used to arrive at a combined model given as ARIMA(1, 0, 1) × SARIMA(1, 0, 1)12 which was subsequently estimated and found to be adequate from the result of the Box-Lung and Q-Q plot respectively. Post forecasting estimation and performance evolution were evaluated using the RMSE and MAE. The results showed that, ARIMA(1, 0, 1) × SARIMA(1, 0, 1)12 is the best forecasting model followed by ARIMA(1, 0, 2) on monthly HIV prevalence in Minna, Niger state.

Dam deformation analysis based on BPNN merging models

Hydropower has made a significant contribution to the economic development of Vietnam,thus it is important to monitor the safety of hydropower dams for the good of the country and the people.In this paper,dam horizontal displacement is analyzed and then forecasted using three methods:the multi-regression model,the seasonal integrated auto-regressive moving average(SARIMA)model and the back-propagation neural network(BPNN)merging models.The monitoring data of the Hoa Binh Dam in Vietnam,including horizontal displacement,time,reservoir water level,and air temperature,are used for the experiments.The results indicate that all of these three methods can approximately describe the trend of dam deformation despite their different forecast accuracies.Hence,their short-term forecasts can provide valuable references for the dam safety.