This paper aims to find a statistical model for the COVID-19 spread in the United Kingdom and Canada. We used an efficient and superior model for fitting the COVID 19 mortality rates in these countries by specifying an optimal statistical model. A new lifetime distribution with two-parameter is introduced by a combination of inverted Topp-Leone distribution and modified Kies family to produce the modified Kies inverted Topp-Leone (MKITL) distribution, which covers a lot of application that both the traditional inverted Topp-Leone and the modified Kies provide poor fitting for them. This new distribution has many valuable properties as simple linear representation, hazard rate function, and moment function. We made several methods of estimations as maximum likelihood estimation, least squares estimators, weighted least-squares estimators, maximum product spacing, Cram$\acute{e}$r-von Mises estimators, and Anderson-Darling estimators methods are applied to estimate the unknown parameters of MKITL distribution. A numerical result of the Monte Carlo simulation is obtained to assess the use of estimation methods. also, we applied different data sets to the new distribution to assess its performance in modeling data.

This paper introduces the new novel four‐parameter Weibull distribution named as the Marshall–Olkin alpha power Weibull (MOAPW) distribution. Some statistical properties of the distribution are examined. Based on Type‐I censored and Type‐II censored samples, maximum likelihood estimation (MLE), maximum product spacing (MPS), and Bayesian estimation for the MOAPW distribution parameters are discussed. Numerical analysis using real data sets and Monte Carlo simulation are accomplished to compare various estimation methods. This novel model’s supremacy upon some famous distributions is explained using two real data sets and it is shown that the MOAPW model can achieve better fits than other competitive distributions.

Objective: This study aimed to determine the prevalence of knee osteoarthritis (OA) in Saudi Arabia and the association between knee OA and modifiable and non-modifiable risk factors. Methods: A self-reported, population-based, cross-sectional survey between January 2021 and October 2021 was conducted. A large, population-representative sample (n = 2254) of adult subjects aged 18 years and over from all regions of Saudi Arabia was collected electronically using convenience sampling. The American College of Rheumatology (ACR) clinical criteria were used to diagnose OA of the knee. The knee injury and osteoarthritis outcome score (KOOS) was used to investigate the severity of knee OA. This study focused on modifiable risk factors (body mass index, education, employment status, marital status, smoking status, type of work, previous history of knee injury, and physical activity level) and non-modifiable risk factors (age, gender, family history of OA, and presence of flatfoot). Results: The overall prevalence of knee OA was 18.9% (n = 425), and women suffered more compared to their male counterparts (20.3% vs. 13.1%, p = 0.001). The logistic regression analysis model showed age (OR: 1.06 [95% CI: 1.05–1.07]; p < 0.01), sex (OR: 2.14 [95% CI: 1.48–3.11]; p < 0.01), previous injury (OR: 3.95 [95% CI: 2.81–5.56]; p < 0.01), and obesity (OR: 1.07 [95% CI: 1.04–1.09]; p < 0.01) to be associated with knee OA. Conclusions: A high prevalence of knee OA underlines the need for health promotion and prevention programmes that focus on modifiable risk factors to decrease the burden of the problem and the cost of treatment in Saudi Arabia.

This paper is concerned with the estimation of the Weibull generalized exponential distribution (WGED) parameters based on the adaptive Type‐II progressive (ATIIP) censored sample. Maximum likelihood estimation (MLE), maximum product spacing (MPS), and Bayesian estimation based on Markov chain Monte Carlo (MCMC) methods have been determined to find the best estimation method. The Monte Carlo simulation is used to compare the three methods of estimation based on the ATIIP‐censored sample, and also, we made a bootstrap confidence interval estimation. We will analyze data related to the distribution about single carbon fiber and electrical data as real data cases to show how the schemes work in practice.

Effective water resource planning is essential for Saudi Arabia, where limited freshwater availability is challenged by rapid population growth, economic development, and climate variability. This study introduces a copula-based modeling framework for forecasting water demand across the country’s urban, industrial, and agricultural sectors. Copulas, compared to traditional models, effectively capture nonlinear and asymmetric relationships among essential variables, including population, temperature, GDP, and sectoral water consumption. Multivariate copula models (Gaussian, Clayton, Gumbel, Frank, t-Copula, and Vine structures) were fitted and evaluated using historical data from 2008 to 2024, obtained from national authorities, including the Ministry of Environment, Water, and Agriculture, the General Authority for Statistics, and the National Center for Meteorology. The 4D normal copula was developed as the most efficient method across all sectors, with MAPE values of 6.37% for urban, 17.51% for industrial, and 23.20% for agricultural consumption. Scenario-based forecasts, which include baseline, high-growth, and sustainability-focused trajectories, indicate that the sustainability scenario yields the best results, resulting in significant demand reductions (21.7% urban, 20.4% industrial, and 8.2% agricultural) with minimal climate impact (+0.4 °C) and the lowest risk levels. The study demonstrates the successful decoupling of water demand from population and economic growth through proper policy interventions, with conditional risk analysis offering actionable early warning capabilities for proactive management. These findings provide a valuable foundation for enhancing national water strategy planning in Saudi Arabia under Vision 2030 and contribute to methodological improvements applicable to water-scarce regions internationally.