Background: Based on data from the Ministry of Health, which highlighted the earlier onset of Covid-19 epidemic in Italy, compared with the Europe, we would like to present a statistical elaboration on the impact of measures taken by the Government, during the phase 1 and the start of phase 2. Methods: After the implementation of a Bayesian changepoint detection method, we looked for a best fit model, based on the first part of time series data, in order to observe the progress of the data in the presence and absence of the restriction measures introduced. Results: Both the implementation of changepoint detection method and the analysis of the curves showed that the decree that marked the start of lockdown has had the effect of slowing down the epidemic by allowing the start of a plateau between 21 and 25 March. Moreover, the decree that decided the beginning of phase 2 on 4 May did not have a negative impact. Conclusion: This statistical analysis supports the hypothesis that stringent measures decreased hospitalization, thanks to a slowing down in the evolution of the epidemic compared with what was expected.

By assessing the trend of air temperature changes, it is possible to explore traces of climatic changes in the area of Iran. Climate change and temperature increase are important human-environmental issues. Based on the change point method, it is possible to identify the onset time of changes in basic variables such as minimum and maximum temperature. Therefore, the present research aims to analyze the change point of temperature thresholds of heat and cold waves in Iran. The temperature threshold means the 95th percentile for maximum temperature values and the 5th percentile for minimum values. For this purpose, the temperature data (minimum and maximum) of 43 synoptic stations in Iran, which have a long statistical period (1966-2018) and suitable distribution, were used. To identify the temperature threshold change time, three change point methods - Pettitt, SNHT, and Buishand's Range Test were used. The results showed that the temperature threshold of heat and cold waves has been increasing over the past few decades. The rising growth rate is equal to 0.019 and 0.052 degrees Celsius per year, respectively. Therefore, in the current study, it was found that the increasing trend of the temperature threshold led to an increase in the frequency of maximum temperatures, and on the other hand, it will lead to an increase in hot days and a decrease in cold nights, in addition to the higher frequency of events, it can be stated that the frequencies will move towards higher values. Based on the results of the spatial-temporal analysis of the thresholds, the increasing trend of the minimum temperature in the northwest, Zagros, and southeast regions of Iran is quite evident. And from the other results of this study, we can emphasize the time of the change point or jump of the temperature threshold of cold and heat waves around 1991. The mentioned year is in line with global studies

Background: The COVID-19 pandemic had caused unexpected strain on healthcare systems in most countries in 2020. Although different survival models were used in clinical decision-making for COVID-19 patients, the effect of different risk factors in patients has not been identified clearly. Elderly patients, especially with comorbidities, were introduced as the most susceptible group at the risk of death. This study aimed to determine the threshold of age that influences chronic diseases and other factors that increase the cure rate of COVID-19 patients. Methods: This observational study was conducted at Shahid Sadoughi hospital in Yazd, Iran. All participants were older than 18 years old with confirmed COVID-19 and completed the day-30 and day-180 follow-ups. The Bayesian method was used through the cure rate models, practical models in survival with a single CHANGE-POINT to detect the threshold of age, illustrating each risk factor’, s effect on the cure rate of patients. Results: The analysis included 901 confirmed COVID-19 cases with a mean age of 54. 93 ±,17. 37 years. From all, 58. 7% (n = 529) were men and 9. 9% (n = 83) death occurrences were recorded. Sixty-five years of age was estimated as the effective changepoint that could change the cure rate of patients at the end of the follow-up times. Conclusion: The cure rate at any time during 30 and 180 follow-up days was noticeably higher in COVID-19 patients younger than 65 years who had cancer.

Statistical process control plays an impressive role in industries due to the growing complicated products and processes. This tool helps practitioners prevent the production of defected products and waste of money and time. Due to the importance of processes and ine,-ciency of methods based on human inspection, the use of image data for statistical process control has gained great attention among researchers in recent years. Image data have been applied by industries for many years for separating defected products and preventing them to get to customers. In recent years, some methods are proposed by researchers in the area of applying statistical features of image data in statistical process control. Image data analysis is decomposed into two categories: spatial domain and frequency domain. The main concentration of previous research studies in the area of process monitoring using image data is in the area of spatial domain analysis. This study has proposed three methods based on one-dimensional wavelet decomposition for monitoring image data with respect to frequency domain features. At each level of decomposition, wavelet transformation decomposes each signal into two elements including an approximation part (which is similar to the main signal and is performed as a low-pass , lter) and a detail element (which is performed as a high-pass , lter). The , rst method proposed in this paper only applies approximation coe, cient for process monitoring. The second and third methods consider the detail coe, cient by using hard thresholding and soft thresholding, respectively. These methods use a likelihood ratio-based statistic for process monitoring. Hence, they can show an out-of-control status and estimate the change point that is one of the most important diagnostic information. The performance of these methods is evaluated and compared with respect to the average run length and the di, erence between real and estimated changepoint criteria. Simulation studies are performed by using a textile image. Results showed a suitable degree of accuracy in detecting out of control status and estimating change points.