Background The COVID-19 pandemic conditions are still prevalent in Iran and other countries and the monitoring system is gradually discovering new cases every day. Therefore, it is a cause for concern around the world, and forecasting the number of future patients and death cases, although not entirely accurate, helps the governments and health-policy makers to make the necessary decisions and impose restrictions to reduce prevalence. Methods In this study, we aimed to find the best model for forecasting the number of confirmed and death cases in Iran. For this purpose, we applied nine models including NNETAR, ARIMA, Hybrid, Holt-Winter, BSTS, TBATS, Prophet, MLP, and ELM network models. The quality of forecasting models is evaluated by three performance metrics, RMSE, MAE, and MAPE. The best model is selected by the lowest value of performance metrics. Then, the number of confirmed and the death cases forecasted for the 30 next days. The used data in this study is the absolute number of confirmed, death cases from February 20 to August 15, 2020. Results Our findings suggested that based on existing data in Iran, the suitable model with the lowest performance metrics for confirmed cases data obtained MLP network and the Holt-Winter model is the suitable model for forecasting death cases in the future. These models forecasted on September 14, 2020, we will have 2,484 new confirmed and 114 new death cases of COVID-19. Conclusion According to the results of this study and the existing data, we concluded that the MLP and Holt-Winter models had the lowest error in forecasting in comparison to other methods. Some models had fitted poorly in the test phase and this is because many other factors that are either not available or have been ignored in this study and can affect the accuracy of forecast results. Based on the trend of data and forecast results, the number of confirmed cases and death cases are almost constant and decreasing, respectively. However, due to disease progression and ignoring the recommendations and protocols of the Ministry of health, there is a possibility of re-emerging this disease more seriously in Iran and this requires more preventive care.
Background The rise of Cesarean Sections (CS) is a global concern. In Iran, the rate of CS increased from 40.7% in 2005 to 53% in 2014. This figure is even higher in the private sector. Objective To analyze the CS rates in the last 2 years using the Robson Classification System in Iran. Methods A retrospective analysis of all in-hospital electronically recorded deliveries in Iran was conducted using the Robson classification. Comparisons were made in terms of the type of hospital, CS rate, and obstetric population, and contributions of each group to the overall cesarean deliveries were reported. Results Two million three hundred twenty-two thousand five hundred women gave birth, 53.6% delivered through CS. Robson group 5 was the largest contributing group to the overall number of cesarean deliveries (47.1%) at a CS rate of 98.4%. Group 2 and 1 ranked the second and third largest contributing groups to overall CSs (20.6 and 10.8%, respectively). The latter groups had CS rates much higher than the WHO recommendation of 67.2 and 33.1%, respectively. “Fetal Distress” and “Undefined Indications” were the most common reasons for cesarean deliveries at CS rates of 13.6 and 13.4%, respectively. There was a significant variation in CS rate among the three types of hospitals for Robson groups 1, 2, 3, 4, and 10. Conclusion The study revealed significant variations in CS rate by hospital peer-group, especially for the private maternity units, suggesting the need for further attention and audit of the Robson groups that significantly influence the overall CS rate. The study results will help policymakers identify effective strategies to reduce the CS rate in Iran, providing appropriate benchmarking to compare obstetric care with other countries that have better maternal and perinatal outcomes.
Background: The basic reproduction number (R0) is an epidemic threshold parameter that indicates the magnitude of disease transmission and thus allows suggestions for the planning of control measures. Objectives: Our aim in this study was to compare different approaches for estimating R0 in the early stage of the SARS-CoV-2 outbreak and discern the best-fitting model. Methods: The dataset was derived from cumulative laboratory-confirmed COVID-19 cases from 26th February to 30th May 2020 in Iran. The methods of exponential growth (EG) rate, maximum likelihood (ML), time-dependent (TD) reproduction number, attack rate (AR), and sequential Bayesian (SB) model were used. The gamma distribution (mean 4.41 ± 3.17 days) was used for serial interval (SI) distribution. The best-fitting method was selected according to the lowest root mean square error (RMSE). Results: We obtained the following estimated R0 [95% confidence interval]: 1.55 [1.54; 1.55], 1.46 [1.45; 1.46], 1.31 [1.30; 1.32], and 1.40 [1.39; 1.41] using EG, ML, TD, and SB methods, respectively. Additionally, the EG and ML methods showed an overestimation of R0, and the SB method showed to be under-fitting in the estimation of R0. The AR method estimated R0 equal to one. The TD method had the lowest RMSE. Conclusions: The simulated and actual R0 of TD showed that this method had a good fit for actual data and the lowest RMSE. Therefore, the TD method is the most appropriate method with the best performance in estimating actual R0 values.
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