Background The World Health Organization End TB Strategy meant that compared with 2015 baseline,the reduction in pulmonary tuberculosis(PTB)incidence should be 20 and 50%in 2020 and 2025,respectively.The case number o...Background The World Health Organization End TB Strategy meant that compared with 2015 baseline,the reduction in pulmonary tuberculosis(PTB)incidence should be 20 and 50%in 2020 and 2025,respectively.The case number of PTB in China accounted for 9%of the global total in 2018,which ranked the second high in the world.From 2007 to 2019,854672 active PTB cases were registered and treated in Henan Province,China.This study was to assess whether the WHO milestones could be achieved in Henan Province.Methods The active PTB numbers in Henan Province from 2007 to 2019,registered in Chinese Tuberculosis Information Management System were analyzed to predict the active PTB registration rates in 2020 and 2025,which is conductive to early response measures to ensure the achievement of the WHO milestones.The time series model was created by monthly active PTB registration rates from 2007 to 2016,and the optimal model was verified by data from 2017 to 2019.The Ljung-Box Q statistic was used to evaluate the model.The statistically significant level isα=0.05.Monthly active PTB registration rates and 95%confidence interval(CI)from 2020 to 2025 were predicted.Results High active PTB registration rates in March,April,May and June showed the seasonal variations.The exponential smoothing winter’s multiplication model was selected as the best-fitting model.The predicted values were approximately consistent with the observed ones from 2017 to 2019.The annual active PTB registration rates were predicted as 49.1(95%CI:36.2–62.0)per 100000 population and 34.4(95%CI:18.6–50.2)per 100000 population in 2020 and 2025,respectively.Compared with the active PTB registration rate in 2015,the reduction will reach 23.7%(95%CI,3.2–44.1%)and 46.8%(95%CI,21.4–72.1%)in 2020 and 2025,respectively.Conclusions The high active PTB registration rates in spring and early summer indicate that high risk of tuberculosis infection in late autumn and winter in Henan Province.Without regard to the CI,the first milestone of WHO End TB Strategy in 2020 will be achieved.However,the second milestone in 2025 will not be easily achieved unless there are early response measures in Henan Province,China.展开更多
Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate mode...Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate models are discussed: the exponential smoothing (ES), Holt-Winters (HW) and autoregressive intergrade moving average (ARIMA) models. To determine the best model, six different strategies were applied as selection criteria to quantify these models’ prediction accuracies. This comparison should help policy makers and industry marketing strategists select the best forecasting method in oil market. The three models were compared by applying them to the time series of regular oil prices for West Texas Intermediate (WTI) crude. The comparison indicated that the HW model performed better than the ES model for a prediction with a confidence interval of 95%. However, the ARIMA (2, 1, 2) model yielded the best results, leading us to conclude that this sophisticated and robust model outperformed other simple yet flexible models in oil market.展开更多
文摘Background The World Health Organization End TB Strategy meant that compared with 2015 baseline,the reduction in pulmonary tuberculosis(PTB)incidence should be 20 and 50%in 2020 and 2025,respectively.The case number of PTB in China accounted for 9%of the global total in 2018,which ranked the second high in the world.From 2007 to 2019,854672 active PTB cases were registered and treated in Henan Province,China.This study was to assess whether the WHO milestones could be achieved in Henan Province.Methods The active PTB numbers in Henan Province from 2007 to 2019,registered in Chinese Tuberculosis Information Management System were analyzed to predict the active PTB registration rates in 2020 and 2025,which is conductive to early response measures to ensure the achievement of the WHO milestones.The time series model was created by monthly active PTB registration rates from 2007 to 2016,and the optimal model was verified by data from 2017 to 2019.The Ljung-Box Q statistic was used to evaluate the model.The statistically significant level isα=0.05.Monthly active PTB registration rates and 95%confidence interval(CI)from 2020 to 2025 were predicted.Results High active PTB registration rates in March,April,May and June showed the seasonal variations.The exponential smoothing winter’s multiplication model was selected as the best-fitting model.The predicted values were approximately consistent with the observed ones from 2017 to 2019.The annual active PTB registration rates were predicted as 49.1(95%CI:36.2–62.0)per 100000 population and 34.4(95%CI:18.6–50.2)per 100000 population in 2020 and 2025,respectively.Compared with the active PTB registration rate in 2015,the reduction will reach 23.7%(95%CI,3.2–44.1%)and 46.8%(95%CI,21.4–72.1%)in 2020 and 2025,respectively.Conclusions The high active PTB registration rates in spring and early summer indicate that high risk of tuberculosis infection in late autumn and winter in Henan Province.Without regard to the CI,the first milestone of WHO End TB Strategy in 2020 will be achieved.However,the second milestone in 2025 will not be easily achieved unless there are early response measures in Henan Province,China.
文摘Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate models are discussed: the exponential smoothing (ES), Holt-Winters (HW) and autoregressive intergrade moving average (ARIMA) models. To determine the best model, six different strategies were applied as selection criteria to quantify these models’ prediction accuracies. This comparison should help policy makers and industry marketing strategists select the best forecasting method in oil market. The three models were compared by applying them to the time series of regular oil prices for West Texas Intermediate (WTI) crude. The comparison indicated that the HW model performed better than the ES model for a prediction with a confidence interval of 95%. However, the ARIMA (2, 1, 2) model yielded the best results, leading us to conclude that this sophisticated and robust model outperformed other simple yet flexible models in oil market.
