Background A method for assessing dental maturity in different populations was first developed in 1973 by Demirjian and has been widely used and accepted since then. While the accuracy for evaluating dental age using Demirjian’s method compared to children’s chronological age has been extensively studied in recent years, the results currently available remain controversial and ambiguous. Methods A literature search of PubMed, Embase, Web of Science, CNKI and CBM databases was conducted to identify all eligible studies published before July 12th, 2013. Weighted mean difference (WMD) with corresponding 95% confidence interval (95% CI) was used to evaluate the applicability of Demirjian’s method for estimating chronological age in children. Results: A meta-analysis was conducted on 26 studies with a total of 11,499 children (5,301 boys and 6,198 girls) aged 3.5 to 16.9 years. Overall, we found that Demirjian’s method overestimated dental age by 0.35 (4.2 months) and 0.39 (4.68 months) years in males and females, respectively. A subgroup analysis by age revealed that boys and girls between the ages of 5 to 14 were given a dental age estimate that was significantly more advanced than their chronological age. Differences between underestimated dental ages and actual chronological ages were lower for male and female 15- and 16-year-old subgroups, though a significant difference was found in the 16-year-old subgroup. Conclusions Demirjian’s method’s overestimation of actual chronological tooth age reveals the need for population-specific standards to better estimate the rate of human dental maturation.

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In real work, we often confront complete linear and nonlinear time series data. But some time series are not pure linear and nonlinear, or complicated one, we need apply two or more models to analyze and predict them. It is necessary to explore and find some novel time series hybrid methods to solve it. Human Immunodeficiency and Virus (HIV) is one of intractable and trouble diseases in the world. Thus, the author of this article wants to analyze and probe into some novel time series methods to get breaking breach in the epidemiology that find some rules in the incidence, distribution, pathogen, and control of HIV in a population.  In this article, to find the best model, auto.arima function is applied to the original time series data to determine autoregressive integrated moving average, ARIMA(0,0,0); ARIMA and generalized autoregressive conditional heteroskedasticity (GARCH), that is, ARIMA-GARCH (1,1) model is used to analyze numbers of people living with HIV for the data of HIV in the world such some important parameters as mu, ar1, ar2, omega, alpha 1, or beta 1 and some specific tests, for example, Jarque-Bera Test, Shapiro-Wilk Test, Ljung-Box Test, etc. Using ARIMA (0, 0, 0) and SARIMA (0,2), seasonal ARIMA, to predict the future values and trends after 2015. Both suggest identical results.

In this paper, the Bayesian structural time series model (BSTS) is used to analyze and predict total confirmed cases who infected COVID-19 in the United States from February 28, 2020 through April 6, 2020 using the collect data from CDC (Center of Disease Control) in the United States. It includes variables of days, total confirmed cases, confirmed cases daily, death cases daily, and fatality rates. The author exploits the flexibility of Local Linear Trend, Seasonality, Contemporaneous covariates of dynamic coefficients in the Bayesian structural time series models. In addition, Causal Impact function in R programming is applied to analyze the model and read report of model. The results of the model show that the total confirmed cases who infected COVID-19 will be still most likely to increase straightly, the total numbers infected COVID-19 would be broken through 600,000 in the United States in near future (in the subsequent months). And then arrive at the peak around mid-May 2020. Also, the model suggests that the probability of variable Recovered cases daily is 0.07.