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A viral respiratory disease, MERS spread by a novel coronavirus, was first detected in Saudi Arabia in 2012. It is a big threat for the Arab community and is a horrible prediction that the disease may rapidly propagate to other parts of the world. In this research endeavor, a mathematical model of MERS-Corona virus (MERS-CoV) is presented. Initially, we formulate a model, governing the dynamics of MERS-CoV disease and then determine basic reproductive number ℛ 0 . Local stability analysis results are formulated at the equilibrium points. It has been found that one of the eigenvalues is zero, therefore bifurcation exists. Afterward, in formulating proper Lyapunov functional [J. P. LaSalle, The Stability of Dynamical Systems, Vol. 25 (Society for Industrial and Applied Mathematics, 1976)], we successfully established results about global stability of the proposed model at both equilibrium points. Sensitivity analysis of the parameters as well as of threshold value for the underlying model has been exhibited. The numerical illustration of theoretical findings is explained via examples. [ABSTRACT FROM AUTHOR]

Any bank's financial management is essential to preparing the assets and liabilities for multiple goals. In this paper, we develop an optimal bank model for the financial management department in the Kingdom of Saudi Arabia. The lexicographic goal programming model was used to formulate the banks' performance management. In this study, the six goals of one of the leading banks in Saudi Arabia, namely, maximize asset, minimize liability, maximize equity, maximize operating income, maximize net income, and maximizing total goal achievements in the financial statement, were studied. To illustrate the model, we have focused on Al Rajhi Bank's financial statements as a case study. The data was obtained from the banks' financial statements. The outcomes of the study exhibited that all goals were accomplished. This proposed model is dynamic because it will help examine the banks' financial strengths located in the kingdom. As a result, the proposed model can guide banking firms in making decisions and developing strategies to deal with numerous monetary circumstances. [ABSTRACT FROM AUTHOR]

Background: Gross domestic product (GDP) serves as a crucial economic indicator for measuring a country's economic growth, exhibiting both linear and non-linear trends. This study aims to analyze and propose an efficient and accurate time series approach for modeling and forecasting the GDP annual growth rate (%) of Saudi Arabia, a key financial indicator of the country. Methodology: Stochastic linear and non-linear time series modeling, along with hybrid approaches, are employed and their results are compared. Initially, conventional linear and nonlinear methods such as ARIMA, Exponential smoothing, TBATS, and NNAR are applied. Subsequently, hybrid models combining these individual time series approaches are utilized. Model diagnostics, including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), are employed as criteria for model selection to identify the best-performing model. Results: The findings demonstrated that the neural network autoregressive (NNAR) model, as a non-linear approach, outperformed all other models, exhibiting the lowest values of MAE, RMSE and MAPE. The NNAR(5,3) projected the GDP of 1.3% which is close to the projection of IMF benchmark (1.9) for the year 2023. Conclusion: The selected model can be employed by economists and policymakers to formulate appropriate policies and plans. This quantitative study provides policymakers with a basis for monitoring fluctuations in GDP growth from 2022 to 2029 and ensuring the sustained progression of GDP beyond 2029. Additionally, this study serves as a guide for researchers to test these approaches in different economic dynamics. [ABSTRACT FROM AUTHOR]

The existing conditions of domestic agricultural production and the resulting products will not be able to fruitfully address the increasing food demand due to the limited fertile land and water resources in Saudi Arabia. Moreover, the escalating threat of a hotter climate, the deterioration in precipitation, and harsh droughts in Saudi Arabia have reduced the predictability of water management efficiency and resulted in the exhaustion of water bodies and serious degradation of ecosystems that have directly affected agricultural systems and indirectly, food security. This study also aims to assess the impact of water efficiency on food insecurity in Saudi Arabia. The study applied the Bayesian Vector Autoregressive (BVAR) model for the reference period for the data extended from 2000–2020. Likewise, we used both impulse response functions (IRFs) and forecasting variance error decomposition (FVED) through 1000 Monte Carlo simulations according to the BVAR model to examine both the response of food insecurity to the shocks on water management efficiency used for various purposes and the decomposition of error variance in food insecurity. The results show that food insecurity was not observed throughout this study. The results of the BVAR analysis indicate that in the short run, the coefficients of water use efficiency are significant based on the Food Insecurity Multidimensional Index (FIMI). Also, the BVAR model provides a better forecast with an interdependence on water use efficiency for agricultural purposes and FIMI. Moreover, the results obtained from IRFs have shown a significant effect of water efficiency on FIMI. Water use efficiency for agriculture and industrial purposes reduces food insecurity while increasing water for services use increases food insecurity. Water use efficiency is the key factor affecting food insecurity in the short run. The results reveal that the water use efficiency shocks will decrease food insecurity. The shocks experienced by food insecurity can be predicted as self-shock over a span of ten years. Emphasis is given to the task of water management that may support food security in Saudi Arabia through implementing and enhancing the water use efficiency as an integral part of achieving the SDGs in Saudi Arabia. [ABSTRACT FROM AUTHOR]

Writing across the curriculum (WAC) has been viewed as a movement that links several academic fields across various academic departments in different institutions. WAC has emerged as a powerful pedagogical tool for improving students' learning outcomes and critical thinking. While the practice of WAC as an independent learning unit is not formally implemented in all Saudi universities, teaching writing across the disciplines is pervasive. The principal objective of this research project is to inform writing specialists and policymakers in Saudi higher education about the status of WAC on Saudi campuses. Through five faculty narratives and group interviews, this research collects and analyzes faculty experiences, challenges, perceptions, and institutional expectations concerning writing practices across the disciplines at predominantly undergraduate, teaching-oriented Saudi universities. Findings indicate that faculty members utilize WAC to encourage critical thinking and improve students' overall linguistic competence. While instructors face challenges, participants reported the need to develop a community of practice to enhance WAC culture in Saudi Arabia. The study recommends that educators form cross-institutional teams to collectively design culturally sensitive, locally rooted, and responsive writing practices. [ABSTRACT FROM AUTHOR]

Introduction: Stroke is a major cause of death and disability globally and in Saudi Arabia as well. Prevention and management of stroke depend highly on raising knowledge and awareness about the disease. Purpose: The purpose of this study was to evaluate Saudi adult's knowledge and awareness about stroke and determine the associated factors. Materials and methods: A cross-sectional online survey was conducted in May--July 2022 among Saudi citizens. Assessments of stroke knowledge about risk factors, symptoms, and response to stroke symptoms were evaluated. Logistic regression was conducted to assess the association between the socio-demographic characteristics and knowledge. Results: A total of 389 participants were enrolled with the majority (81.7%) being male participants. Less than half of the study subjects (43.3%) identified four out of five correct answers related to general knowledge about stroke. Almost all the participants were able to identify at least one risk factor associated with stroke. The majority of the participants (81.2%) believed that physical inactivity was the most common risk factor associated with stroke. Approximately three-quarters of participants considered difficulty speaking and understanding speech, followed by the sudden loss of consciousness as the most common stroke manifestation. Participants with a history of hypertension, dyslipidemia, and obesity had significantly higher odds of identifying at least one early stroke symptom (OR 2.271 [95% CI 1.402 3.677], 2.059 [95% CI 1.273 3.328], and 2.665 [95% CI 1.431 4.963], respectively). Conclusion: Our study revealed that participants have good knowledge about stroke. Nonetheless, further e orts are required to raise awareness and educate the public to optimize and ensure better treatment outcomes. [ABSTRACT FROM AUTHOR]

The research investigates the impact of internal lean practices (ILP), green operations practices (GOP), and enterprise resource planning (ERP) on operational performance (OP) in Saudi Arabian manufacturing companies, with a focus on the mediating role of supply chain integration (SCI). Data from 219 employees informed the study, which employed a quantitative research approach and a cross-sectional design. SPSS multiple regression analysis revealed positive and statistically significant effects of ILP, GOP, and ERP on both SCI and OP. ILP significantly influenced SCI and, indirectly through SCI, positively affected OP. Similarly, GOP demonstrated a positive impact on both SCI and OP, indicating a mediating relationship. ERP practices significantly influenced both SCI and OP, with SCI mediating the relationship between ERP dimensions and OP. These findings contribute to theoretical understanding and offer practical insights for Saudi Arabian manufacturing companies, highlighting the strategic importance of improving ERP systems, GOP, and ILP to enhance SCI and consequently OP. The research underscores the critical role of SCI in advancing sustainability and operational efficiency. [ABSTRACT FROM AUTHOR]

AIMRC), Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia; 2King Saud Bin-Abdulaziz University for Health Sciences (KSAU-HS), Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia; 3Research Training and Development Section, King Abdullah International Medical Research Center (KAIMRC), Ministry of National Guard-Health Affairs, Riyadh, Saudi ArabiaCorrespondence: Mostafa A Abolfotouh, Research Training and Development Section, King Abdullah International Medical Research Center (KAIMRC), Ministry of National Guard-Health Affairs, P.O. Box 3660, Mail Code 1515 (KAIMRC), Riyadh, 11481, Saudi Arabia, Tel +966 11 429-4460 ; +966 503659204, Fax +966 11429-4440, Email [email protected] Background: With the advances in genomics research, many countries still need more bioinformatics skills. This study aimed to assess the levels of awareness of bioinformatics and predictors of its use in genomics research among scientists in Saudi Arabia.Methods: In a cross-sectional survey, 309 scientists of different biological and biomedical specialties were subjected to a previously validated e-questionnaire to collect data on (1) Knowledge about bioinformatics programming languages and tools, (2) Attitude toward acceptance of bioinformatics resources in genome-related research, and (3) The pattern of information-seeking to online bioinformatics resources. Logistic regression analysis was applied to identify the predictors of using bioinformatics in research. Significance was set at p< 0.05.Results: More than one-half (248, 56.4%) of all scientists reported a lack of bioinformatics knowledge. Most participants had a neutral attitude toward bioinformatics (295, 95.4%). The barriers facing acceptance of bioinformatics tools reported were; lack of training (210, 67.9%), insufficient support (180, 58.2%), and complexity of software (138, 44.6%). The limited experience was reported in; having one or more bioinformatics tools (98, 31.7%), using a supercomputer in their research inside (44, 14.2%) and outside Saudi Arabia (55, 17.8%), the need for developing a program to solve a biological problem (129, 41.7%), working in one or more fields of bioinformatics (93, 30.1%), using web applications (112, 36.2%), and using programming languages (102, 33.0%). Significant predictors of conducting genomics research were; younger scientists (p=0.039), Ph.D. education (p=0.003), more than five years of experience (p< 0.05), previous training (p< 0.001), and higher bioinformatics knowledge scores (p< 0.001).Conclusion: The study revealed a short knowledge, a neutral attitude, a lack of resources, and limited use of bioinformatics resources in genomics research. Education and training during each education level and during the job is recommended. Cloud-based resources may help scientists do research using publicly available Omics data. Further studies are necessary to evaluate collaboration among bioinformatics software developers and biologists. [ABSTRACT FROM AUTHOR]

The epistemic uncertainty in coronavirus disease (COVID-19) model-based predictions using complex noisy data greatly affects the accuracy of pandemic trend and state estimations. Quantifying the uncertainty of COVID-19 trends caused by different unobserved hidden variables is needed to evaluate the accuracy of the predictions for complex compartmental epidemiological models. A new approach for estimating the measurement noise covariance from real COVID-19 pandemic data has been presented based on the marginal likelihood (Bayesian evidence) for Bayesian model selection of the stochastic part of the Extended Kalman filter (EKF), with a sixth-order nonlinear epidemic model, known as the SEIQRD (Susceptible–Exposed–Infected–Quarantined–Recovered–Dead) compartmental model. This study presents a method for testing the noise covariance in cases of dependence or independence between the infected and death errors, to better understand their impact on the predictive accuracy and reliability of EKF statistical models. The proposed approach is able to reduce the error in the quantity of interest compared to the arbitrarily chosen values in the EKF estimation. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the relationship between Arabic Language Proficiency (ALP) and expatriate adjustment (EA), and job performance (JP) in Saudi Arabia. In addition, the moderating role of personal and environmental factors is investigated. This investigation employs a mixed-methods research design. The intended audience is the personnel of Saudi Arabia's Higher Educational Institutions (HEIs). A survey was conducted for quantitative data, while interviews were used for qualitative data. CFA and SEM were used for quantitative data analysis, while thematic analysis was used for qualitative data analysis. The results of this study indicate that Arabic language proficiency significantly affects job performance and expatriate adaptation. It was also discovered that several personal and environmental factors contribute to the impact, including language barriers, the workplace environment, training, and support. This study is novel because it examines the effects of ALP on EA and JP in Saudi Arabia, in addition to personal and environmental factors; no prior study of this nature has been conducted in Saudi Arabia. The study is limited to Higher Education Institutions (HEIs); thus, its generalizability is restricted. The study is significant for Saudi Arabia's Higher Education Institutions (HEIs) because it emphasizes the significance of training and support, which would aid in adapting foreign employees, and enhancing their job performance. [ABSTRACT FROM AUTHOR]

A local performance of the SIR model on actual data is introduced. A good approximation of the SIR model parameters in Saudi Arabia during a period of 275 days (the first of April 2020 to the end of December 2020) is determined. The parameters are estimated from the recorded data and used to predict the values in the next subsequent period. The performance of the standard fourth order Runge–Kutta method is considered for the classical SIR models over different periods. A comparison of the recorded data and the predicted values during the considered period illustrated the effectiveness of the treatment. The mathematical properties and initial conditions are considered within the estimated parameter values. It is shown that lockdown and social distance attitudes effectively controlled the spread of the disease. The maximum number of daily active infected cases is 63,026, and occurs in July and this agrees with the calculated values. To make the graphs representable, we considered a fixed closed population, the effective sample during the considered period of size N = 400,000 only (represents only 1% of the overall population susceptible, this must be associated, with great thanks, to the authorities in KSA). [ABSTRACT FROM AUTHOR]

Introduction: An occupational stress was reported as an inhibitor of optimal performance among nurses. Emotional intelligence (EI) has emerged as a successful behavioral buffer against occupational stress and as a facilitator for better performance. This study aimed to investigate the potential relationship between nurses' EI and their work performance; and to examine the mediating role of occupational stress. Design: A predictive correlational design was adopted. Method: Self‐reported questionnaires were administered to 391 full‐time bedside nurses recruited from one of the big hospitals in Saudi Arabia, between April and June 2021. Data were coded and analyzed using IBM SPSS version 25.0. Simple and multiple linear regression analyses were used to test the hypotheses. The significance level for all tests was set at p ≤ 0.05. Bonferroni correction method was used to control the family‐wise error rate. Results: The findings revealed an affirmative association between nurses' EI and work performance (β = 0.69, p < 0.001; r2 = 0.483). Additionally, an inverse association was established between nurses' EI and their perception of occupational stress (β = −0.54, p < 0.001; r2 = 0.286), and between nurses' perception of occupational stress and work performance (β = −0.52; p < 0.001; r2 = 0.226). Additionally, our results showed that occupational stress played a mediating role in the relationship between nurses' EI and work performance. Conclusion: This study presented a novel framework that includes two factors affecting work performance among nurses in Saudi Arabia. Our results suggest that EI is vital for effective work performance among nurses. Additionally, EI was found to be a useful coping strategy against occupational stress. Clinical Relevance: EI has been described as a valuable asset for better performance and effective group cohesiveness among nurses. Optimal nurses᾽ performance leads to meeting patients᾽ needs and organizational goals. [ABSTRACT FROM AUTHOR]

In this study, we determine and compare the incubation duration, serial interval, presymptomatic transmission, and case fatality rate of MERS-CoV and COVID-19 in Saudi Arabia based on contact tracing data we acquired in Saudi Arabia. The date of infection and infector-infectee pairings are deduced from travel history to Saudi Arabia or exposure to confirmed cases. The incubation times and serial intervals are estimated using parametric models accounting for exposure interval censoring. Our estimations show that MERS-CoV has a mean incubation time of 7.21 (95% CI: 6.59e7.85) days, whereas COVID-19 (for the circulating strain in the study period) has a mean incubation period of 5.43(95% CI: 4.81 e6.11) days. MERS-CoV has an estimated serial interval of 14.13(95% CI: 13.9e14.7) days, while COVID-19 has an estimated serial interval of 5.1(95% CI: 5.0e5.5) days. The COVID-19 serial interval is found to be shorter than the incubation time, indicating that presymptomatic transmission may occur in a significant fraction of transmission events. We conclude that during the COVID-19 wave studied, at least 75% of transmission happened prior to the onset of symptoms. The CFR for MERS-CoV is estimated to be 38.1% (95% CI: 36.8 e39.5), while the CFR for COVID-19 1.67% (95% CI: 1.63e1.71). This work is expected to help design future surveillance and intervention program targeted at specific respiratory virus outbreaks, and have implications for contingency planning for future coronavirus outbreaks. [ABSTRACT FROM AUTHOR]

In the early stages of the pandemic, Saudi Arabia and other countries in the Arab Gulf region relied on non-pharmaceutical therapies to limit the effect of the pandemic, much like other nations across the world. In comparison to other nations in the area or globally, these interventions were successful at lowering the healthcare burden. This was accomplished via the deterioration of the economy, education, and a variety of other societal activities. By the end of 2020, the promise of effective vaccinations against SARS-CoV-2 have been realized, and vaccination programs have begun in developed countries, followed by the rest of the world. Despite this, there is still a long way to go in the fight against the disease. In order to explore disease transmission, vaccine rollout and prioritisation, as well as behavioural dynamics, we relied on an age-structured compartmental model. We examine how individual and social behaviour changes in response to the initiation of vaccination campaigns and the relaxation of non-pharmacological treatments. Overall, vaccination remains the most effective method of containing the disease and resuming normal life. Additionally, we evaluate several vaccination prioritisation schemes based on age group, behavioural responses, vaccine effectiveness, and vaccination rollout speed. We applied our model to four Arab Gulf nations (Saudi Arabia, Bahrain, the United Arab Emirates, and Oman), which were chosen for their low mortality rate compared to other countries in the region or worldwide, as well as their demographic and economic settings. We fitted the model using actual pandemic data in these countries. Our results suggest that vaccinations focused on the elderly and rapid vaccine distribution are critical for reducing disease resurgence. Our result also reinforces the cautious note that early relaxation of safety measures may compromise the vaccine's short-term advantages. [ABSTRACT FROM AUTHOR]

One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations. [ABSTRACT FROM AUTHOR]

The flare-up of coronavirus disease 2019 (COVID-19) was first identified in China and then spread worldwide. The concerning articles are significant because of their increasing average infection rate. The exact scientific anatomy of this circumstance remains to be discussed. Nevertheless, it is important for governments and all responsible organizations to have sound statistics and analysis to decide on possible necessary actions. This article compares the dynamics of the COVID-19 pandemic between Saudi Arabia and Egypt. We provide an appropriate process of convergence of facts that can be useful for both administrations to implement appropriate quarantine procedures. In addition, the new generalized modified Weibull distribution is provided to provide the preferred description about COVID-19 total cases, daily new cases, total deaths, and daily death statistics of Saudi Arabia and Egypt. [ABSTRACT FROM AUTHOR]

The present paper is concerned with the study of the quality of life index. Such an index has become an important index for measuring the well-being of individuals. However, the quality of life index is always subjective, intangible, and often hard to quantify with precision due to the lack of quantitative models. The main goal of the present paper is thus to propose a mathematical, quantitative model for the measurement of a quality of life index. The main novelty is firstly the construction of a wavelet dynamic multiscale model to quantify and investigate the effect of time scale on the quality of life index measuring. The proposed procedure is acted empirically on a sample corresponding to Saudi Arabia as a case study during the period from 2003 to 2020 as part of the 2030 vision plan. Saudi Arabia has implemented the so-called 2030 vision plan where quality of life improvement is one of the main goals. The findings show that wavelets are capable of localizing the time-wise behavior of the index contrarily to classical studies, which estimate a global view of the index. Moreover, the study shows the link between the quality of life behavior and many other indices. [ABSTRACT FROM AUTHOR]

The agricultural sector in Saudi Arabia constitutes an essential pillar of the national economy and food security. Crop diseases are a major problem of the agricultural sector and greatly affect the development of the economies in various countries around the world. This study employed three prediction models, namely CNN, SVM, and KNN, with different image processing methods to detect and classify apple plant leaves as healthy or diseased. These models were evaluated using the Kaggle New Plant Diseases database. This study aims to help farmers detect and prevent diseases from spreading. The proposed method provides recommendations for the appropriate solutions for each type of recognized plant disease based on the classification results. [ABSTRACT FROM AUTHOR]

Education is the process of acquiring knowledge, skills, values, peers, beliefs, practices and personal development. Mentoring methods include teaching, coaching, storytelling, discussion, and guided research. Education is often led by teachers; however, students can also engage in self-education. As education is a continuous process, it requires rectifying from time to time and that is achieved by conducting regular evaluation and assessment. The role of assessment cannot be denied in any educational process because it works as a course-correction tool. It is indeed one of the pillars of formal education. However, validation of learning results is rarely undertaken in institutions because no comprehensive method for this is available. The current study evaluates international and national best result assessment practices to prepare a balanced model for use in English language Departments in ESL/EFL situations. The criteria are applied at Mustaqbal University (MU), Saudi Arabia, and results showed that only some of the forty seven criteria are robustly applied, a few partially applied, and some not applied at all. Overall, the study establishes that result assessment needs a careful rethinking at MU to place it among the most prestigious educational institutions of the world. [ABSTRACT FROM AUTHOR]

Since the discovery and the development of many technological instruments such as modern communication and communication technologies, digital media has served widely from the technology, and thus intersected quasi all life's domains, by providing news, information, and also discussions of quasi all subjects. In social sciences, for example, a major part of digital media sites discusses the well-being of both individuals, and societies, especially with the presence of many challenging phenomena like pandemics, wars, and financial crises. A major concern for QoL index for example resides in whether it is a uniform stable index or on the contrary a non-uniform index depending strongly and point-wisely on the time scale. This motivates the consideration of a non-uniform QoL index based on non-uniform wavelets to take into account the influence of many factors issued from political, social, environmental, economic effects according to the time scale. The model developed is tested on Saudi Arabia QoL index case. Recall that the Kingdom of Saudi Arabia has fixed many aims in its 2030-vision plan including the quality of life as a major goal. For example, the KSA worldwide NEOM project necessitates surely a supportive environment to be realized. The second purpose of our paper is also to study the impact of digital media such as social one on the QoL. Therefore, the data collected is basically issued from a set of conversations on social media platforms. To obtain a suitable time series, we developed a backward-forward non-uniform wavelet forecasting model to extend the data sample suitably. The whole time series is by the next applied for the QoL index dynamics and interpretations. The findings showed that effectively, the QoL index is never a stable single index on the whole year, but on the contrary, the dependence on the time scale is non-uniform. Moreover, to interpret suitably and for a correct policy, the non-uniform behavior should be considered adequately. [ABSTRACT FROM AUTHOR]

Renewable energy forecasting services comprise various modules for intra-day and day-ahead forecasts. This work specifically addresses day-ahead forecasts, utilizing specifications based on endogenous, historical measurements. These specifications are designed to be computationally efficient, requiring fewer input variables and less training data. Such weather-independent specifications serve as benchmarks against the more computationally demanding forecasts based on numerical weather predictions. A series of experiments, designed to simulate the real-world application of an online system, were conducted on sliding windows of back-contact photovoltaic (installed at KAUST, Saudi Arabia) output series, solar irradiance recorded in Hawaii, and simulated data. Our analysis evaluated 24 specifications, which are variants of (i) functional time series models (including two novel shrinkage procedures); (ii) time series nearest neighbor schemes; (iii) exponential smoothing procedures; (iv) autoregressive integrated moving average processes; (v) automatic techniques based on time series decomposition; and (vi) the persistence model. In addition to employing outlier-robust accuracy metrics, such as mean absolute error, our evaluation also prioritized prediction-interval accuracy, quantified by the mean scaled interval score. Our findings suggest that practitioners can achieve significant improvements over the persistence model by forecasting daily profiles using adaptive nonparametric or functional data analysis-based procedures. Moreover, applying shrinkage to nearest neighbor (NN) forecasts toward smooth, average daily profiles significantly enhances NN performance. Conversely, some popular, computationally intensive models fail to perform adequately to justify their additional cost. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for coronavirus disease-19 (COVID-19). This virus has caused a global pandemic, marked by several mutations leading to multiple waves of infection. This paper proposes a comprehensive and integrative mathematical approach to the third wave of COVID-19 (Omicron) in the Kingdom of Saudi Arabia (KSA) for the period between 16 December 2022 and 8 February 2023. It may help to implement a better response in the next waves. For this purpose, in this article, we generate a new mathematical transmission model for coronavirus, particularly during the third wave in the KSA caused by the Omicron variant, factoring in the impact of vaccination. We developed this model using a fractal-fractional derivative approach. It categorizes the total population into six segments: susceptible, vaccinated, exposed, asymptomatic infected, symptomatic infected, and recovered individuals. The conventional least-squares method is used for estimating the model parameters. The Perov fixed point theorem is utilized to demonstrate the solution's uniqueness and existence. Moreover, we investigate the Ulam–Hyers stability of this fractal–fractional model. Our numerical approach involves a two-step Newton polynomial approximation. We present simulation results that vary according to the fractional orders (γ) and fractal dimensions (θ), providing detailed analysis and discussion. Our graphical analysis shows that the fractal-fractional derivative model offers more biologically realistic results than traditional integer-order and other fractional models. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has led to increased use of online resources in educational institutions, making e-learning a necessity. This study aimed to investigate how students from four colleges at Jazan University in Saudi Arabia perceived and accepted e-learning and e-evaluation. Methods: 236 students participated in a cross-sectional study conducted in October 2021. The students completed a well-constructed questionnaire with 22 closed-ended questions divided into five domains. The students rated their answers on a 5-point Likert scale from 1 to 5. To analyze the collected data, the researchers utilized SPSS (v26). Result: Out of the total 236 students who participated in the study, most (30.5%) from the College of Public Health and Tropical Medicine (59.8%) used laptops, while 78% of students had a favorable impression of e-learning. Students' perceptions were focused on whether e-learning and e-evaluation methods helped them understand the study material smoothly and clearly. Among the students, 27% strongly agreed, 25% agreed, 16% were neutral, 11% disagreed, and 11% strongly disagreed. Additionally, 28% of the students strongly agreed, and 34% agreed that exam questions during e-evaluation were appropriate and comprehensive. Although online learning may result in less social contact, a lack of social presence, and difficulties in communication harmonization, e-learning still has some positive effects on students. It is considered a powerful platform, especially during emergencies or for those unable to attend in-person classes to complete their studies. The study has significant implications for higher education institutions, especially during emergencies, where online learning is necessary. Future research can further explore the factors that affect student perceptions and acceptance of elearning and e-evaluation and how to improve them. [ABSTRACT FROM AUTHOR]

A new mathematical model for the transmission dynamics and control of the Middle Eastern respiratory syndrome (MERS), a respiratory virus caused by MERS-CoV coronavirus (and primarily spread to humans by dromedary camels) that first emerged out of the Kingdom of Saudi Arabia (KSA) in 2012, was designed and used to study the transmission dynamics of the disease in a human-camel population within the KSA. Rigorous analysis of the model, which was fitted and cross-validated using the observed MERS-CoV data for the KSA, showed that its disease-free equilibrium was locally asymptotically stable whenever its reproduction number (denoted by $ {\mathbb R}&#95;{0M} $) was less than unity. Using the fixed and estimated parameters of the model, the value of $ {\mathbb R}&#95;{0M} $ for the KSA was estimated to be 0.84, suggesting that the prospects for MERS-CoV elimination are highly promising. The model was extended to allow for the assessment of public health intervention strategies, notably the potential use of vaccines for both humans and camels and the use of face masks by humans in public or when in close proximity with camels. Simulations of the extended model showed that the use of the face mask by humans who come in close proximity with camels, as a sole public health intervention strategy, significantly reduced human-to-camel and camel-to-human transmission of the disease, and this reduction depends on the efficacy and coverage of the mask type used in the community. For instance, if surgical masks are prioritized, the disease can be eliminated in both the human and camel population if at least 45% of individuals who have close contact with camels wear them consistently. The simulations further showed that while vaccinating humans as a sole intervention strategy only had marginal impact in reducing the disease burden in the human population, an intervention strategy based on vaccinating camels only resulted in a significant reduction in the disease burden in camels (and, consequently, in humans as well). Thus, this study suggests that attention should be focused on effectively combating the disease in the camel population, rather than in the human population. Furthermore, the extended model was used to simulate a hybrid strategy, which combined vaccination of both humans and camels as well as the use of face masks by humans. This simulation showed a marked reduction of the disease burden in both humans and camels, with an increasing effectiveness level of this intervention, in comparison to the baseline scenario or any of the aforementioned sole vaccination scenarios. In summary, this study showed that the prospect of the elimination of MERS-CoV-2 in the Kingdom of Saudi Arabia is promising using pharmaceutical (vaccination) and nonpharmaceutical (mask) intervention strategies, implemented in isolation or (preferably) in combination, that are focused on reducing the disease burden in the camel population.

Introduction: Respiratory syncytial virus (RSV) represents a considerable burden on the healthcare system and hospital resources. This study explored the impact of universal immunoprophylaxis with long-acting monoclonal antibody (nirsevimab) during infants' first RSV season on RSV-induced health events and related costs in the Kingdom of Saudi Arabia (KSA)., Methods: The burden of RSV-induced health events and related costs under the current standard of practice (SoP) and the impact of universal immunoprophylaxis with nirsevimab was estimated using a static decision-analytic model in a cohort of infants experiencing their first RSV season in the KSA. The model estimated hospital admissions (including pediatric intensive care unit [PICU] admissions and mechanical ventilation [MV]), emergency room (ER) visits, primary care (PC) visits, long-term sequelae, and RSV mortality., Results: The model estimated that under the current SoP, RSV results in 17,179-19,607 hospitalizations (including 2932-3625 PICU and 172-525 MV admissions), 57,654-191,115 ER visits, 219,053-219,970 PC visits, 14 deaths, 12,884-14,705 cases of recurrent wheezing, and a total cost of SAR 480-619 million. Universal nirsevimab immunoprophylaxis was estimated to avert 58% of hospitalizations (58% PICU admissions, 58% MV episodes), 53% of ER visits, 53% of PC visits, 58% of episodes of recurrent wheezing, 8 deaths, and result in savings of SAR 274-343 million in total healthcare cost., Conclusion: Compared with current SoP, an nirsevimab immunoprophylaxis strategy in the KSA for all infants during their first RSV season was estimated to dramatically decrease healthcare resource use, and economic burden associated with RSV., (© 2024. The Author(s).)

Aim: To evaluate cost-effectiveness of upadacitinib (targeted synthetic-disease modifying anti-rheumatic drug [ts-DMARD]) as first-line (1 L) treatment versus current treatment among patients with rheumatoid arthritis (RA) in the Kingdom of Saudi Arabia (KSA), who had an inadequate response to prior conventional-synthetic (csDMARDs) and/or biologic-DMARDs (bDMARDs)., Methods: This Excel-based model included patients with moderate (Disease Activity Score [DAS28]: >3.2 to ≤5.1) or severe RA (DAS28 > 5.1). Cost-effectiveness of current treatment (1 L: adalimumab-originator/biosimilar; second-line (2 L): other bDMARDs/tofacitinib) was compared against a new treatment involving two scenarios (1 L: upadacitinib, 2 L: adalimumab-biosimilar [scenario-1]/adalimumab-originator [scenario-2]) for a 10-year time-horizon from societal perspective. Model outcomes included direct and indirect costs, quality-adjusted life-years (QALYs), hospitalization days, number of orthopedic surgeries, and incremental cost-utility ratio (ICUR) per QALY., Results: With the current pathway, estimated total societal costs for 100 RA patients over 10-year period were Saudi Riyal (SAR) 50,450,354 (United States dollars [USD] 13,453,428) (moderate RA) and SAR50,013,945 (USD13,337,052) (severe RA). New pathway (scenario-1) showed that in patients with moderate-to-severe RA, upadacitinib led to higher QALY gain (+8.99 and +15.63) at lower societal cost (cost difference: -SAR2,023,522 [-USD539,606] and -SAR3,373,029 [-USD899,474], respectively). Thus, as 1 L, upadacitinib projects "dominant" ICUR per QALY over current pathway. Moreover, in alternate pathway (scenario-2), upadacitinib also projects "dominant" ICUR per QALY for patient with severe RA (QALY gain: +15.63; cost difference: -SAR 164,536 [-USD43,876]). However, moderate RA was associated with additional cost of SAR1,255,696 (USD334,852) for improved QALY (+8.99) over current pathway (ICUR per QALY: SAR139,742 [USD37,264]). Both scenarios resulted in reduced hospitalization days (scenario-1: -14.83 days; scenario-2: -11.41 days) and number of orthopedic surgeries (scenario-1: -8.36; scenario-2: -6.54) for moderate-to-severe RA over the current treatment pathway., Conclusion: Upadacitinib as 1 L treatment in moderate-to-severe RA can considerably reduce healthcare resource burden in KSA, majorly due to reduced drug administration/monitoring/hospitalization/surgical and indirect costs.

Aims: This study estimated, for Saudi Arabia, the cost-efficiency of converting patients from reference Neupogen and Neulastim to one of two filgrastim biosimilars (Nivestim, Zarzio); the budget-neutral expanded access to supportive care with biosimilar filgrastim and therapeutic care to ado-trastuzumab emtansine thus afforded; and the number-needed-to-convert (NNC) to provide supportive or therapeutic treatment to one patient., Materials and Methods: Replicating prior studies, we modeled the cost-efficiencies gained from converting varying proportions of a hypothetical panel of 4,000 patients undergoing six cycles of cancer treatment from Neupogen or Neulastim to one of the two biosimilar G-CSF formulations, using national cost inputs. Cost-savings in USD were used to estimate the additional doses of biosimilar G-CSF and expanded access to ado-trastuzumab emtansine on a budget-neutral basis, and NNC to purchase one additional dose of supportive or therapeutic treatment., Results: Savings from conversion from reference to a biosimilar filgrastim were $3,086,400 (Nivestim) and $3,460,800 (Zarzio). With reference pegfilgrastim, savings from conversion were $11,712,240 (Nivestim) and $12,086,640 (Zarzio). Biosimilar conversion from reference to biosimilar filgrastim enabled expanded access to ado-trastuzumab emtansine ranging from 61 patients (5 days, Nivestim) to 191 patients (14 days, Zarzio). For supportive care, biosimilar conversion enabled expanded access ranging from 8,244 patients (5 days, Nivestim) to 25,882 patients (14 days, Zarzio). For biosimilar conversion from daily filgrastim, the NNC for treatment with ado-trastuzumab emtansine decreased as days of injections increased [5 days: 395 (Nivestim), 352 (Zarzio); 14 days: 141(Nivestim), 126 (Zarzio)]. Alternately, for biosimilar conversion from single-injection pegfilgrastim to daily biosimilar filgrastim, the NNC for treatment with ado-trastuzumab emtansine rose as days of injections increased, being highest under the 14-day scenario (146, Nivestim; 130, Zarzio)., Conclusion: This simulation study demonstrated significant potential cost-savings from biosimilar conversion. These savings provide budget-neutral increased access to supportive and therapeutic cancer care.

Background: The Kingdom of Saudi Arabia (KSA) has embarked on a Health Sector Transformation Program as part of the Kingdom's Vision 2030 initiatives with the facilitation of access to healthcare services for the millions in KSA with diabetes an essential part of the Program. Decision-making tools, such as budget impact models, are required to consider the addition of new medications like oral semaglutide that have multifaceted health benefits and address barriers related to therapeutic inertia to reduce diabetes-related complications., Objective: To determine the financial impact of the introduction of oral semaglutide as a treatment option for people with type 2 diabetes mellitus (T2DM) in KSA., Methods: From the public payer's perspective, the budget impact model estimates the costs before and after the introduction of oral semaglutide over a 5-year time horizon. The budget impact of introducing oral semaglutide (primary comparator) compared with three different classes of diabetes medicines: glucagon-like peptide-1 receptor agonists (GLP-1), sodium-glucose transport protein 2 inhibitors (SGLT 2i) and dipeptidyl peptidase 4 inhibitors (DDP-4i) have been calculated based on the projected market shares. The model includes the cost of care through the incorporation of health outcomes that have an impact on the national payer's budget in Saudi Riyals (SAR)., Results: The budget impact over the five-year time horizon indicates a medication cost increase (17,424,788 SAR), and cost offsets which include a difference in diabetes management costs (-3,625,287 SAR), CV complications costs (-810,733 SAR) and weight loss savings of 453,936 SAR. The cumulative total cost difference is 12,427,858 SAR (0.66%)., Conclusion: The introduction of oral semaglutide 14 mg as a second-line treatment option after metformin is indicated as budget-neutral to slightly budget-inflating for the public pharmaceutical formulary of KSA. The price difference is offset by positive health outcomes and costs. This conclusion was confirmed through a probabilistic sensitivity analysis.