The constant-stress partially accelerated life test (CSPALT) model with Type-II hybrid censoring scheme (Type-II HCS) is the subject of our research. Units have a lifetime that follows the generalized Rayleigh distribution. Bayesian and E-Bayesian estimates are derived by applying two of the loss functions, mainly the squared error loss (SEL) and LINEX loss functions. Bayesian and E-Bayesian estimates are obtained using Markov chain Monte Carlo (MCMC) methods. To prove the applicability and the importance of the subject, a test for real data will be provided. To evaluate the distribution’s effectiveness, we utilized a variety of datasets and proposed several kinds of censoring. Finally, all results are compared in order to determine the effectiveness of the proposed methods. All major findings are concluded in the conclusion section.

In this article, a new hybrid time series model is proposed to predict COVID-19 daily confirmed cases and deaths. Due to the variations and complexity in the data, it is very difficult to predict its future trajectory using linear time series or mathematical models. In this research article, a novel hybrid ensemble empirical mode decomposition and error trend seasonal (EEMD-ETS) model has been developed to forecast the COVID-19 pandemic. The proposed hybrid model decomposes the complex, nonlinear, and nonstationary data into different intrinsic mode functions (IMFs) from low to high frequencies, and a single monotone residue by applying EEMD. The stationarity of each IMF component is checked with the help of the augmented Dicky–Fuller (ADF) test and is then used to build up the EEMD-ETS model, and finally, future predictions have been obtained from the proposed hybrid model. For illustration purposes and to check the performance of the proposed model, four datasets of daily confirmed cases and deaths from COVID-19 in Italy, Germany, the United Kingdom (UK), and France have been used. Similarly, four different statistical metrics, i.e., root mean square error (RMSE), symmetric mean absolute parentage error (sMAPE), mean absolute error (MAE), and mean absolute percentage error (MAPE) have been used for a comparison of different time series models. It is evident from the results that the proposed hybrid EEMD-ETS model outperforms the other time series and machine learning models. Hence, it is worthy to be used as an effective model for the prediction of COVID-19.

Statistician always tries to find an easy method that gives a suitable fit. However, proposing a new distribution always solves many statistical problems. In this paper, we introduce a new extension of the Lindley distribution. We made a statistical inference under complete and Type-II censored sample on a new extension of the Lindley distribution. We have deduced its PDF and CDF using a cubic transmuted family that is because the new equations are very easy in computation. We called the new form the cubic transmuted Lindley distribution. The probability distribution function and the cumulative distribution function were also written as a closed form along with some mathematical properties. The classical method, which is the maximum likelihood estimation technique and maximum product of spacing technique, was used to find the estimators of the unknown model parameters. At last, to prove the superiority and applicability of the model, three real data sets are implemented and compared using the proposed method. We made a comparison with some of its baseline distributions and some other extensions, and our model outperforms the published ones.

Background: The potential for COVID-19 transmission has increased sharply, so the government must implement various strategies to control the spread, especially in Jambi Province. The number of positive confirmed cases of COVID-19 in Jambi Province until August 26, 2021, was 27,422 people, with a case fatality rate is 2.37%. This condition illustrates that the spread of COVID-19 is increasing every day, so the government has set a lockdown at Level 4. Method: This research aims to analyze the profile of COVID-19 patients in Jambi Province (secondary data analysis) with a cross-sectional study design. Data analysis includes univariate analysis with the mean difference test and Chi-Square test. Result: The results show that the age of COVID-19 patients is significantly different between men and women. Furthermore, based on the Chi-Square test, it shows a significant relationship between age and gender and between region and age with a p-value <0.05. Conclusion: Indeed, the risk of COVID-19 cases increases with age and differs for each gender with a high level of mobility.

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