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Background: Health policy and systems research (HPSR) can strengthen health systems and improve population health outcomes. In the Eastern Mediterranean Region (EMR), there is limited recognition of the importance of HPSR and funding remains the main challenge. This study seeks to: (1) assess the reporting of funding in HPSR papers published between 2010 and 2022 in the EMR, (2) examine the source of funding in the published HPSR papers in the EMR and (3) explore variables influencing funding sources, including any difference in funding sources for coronavirus disease 2019 (COVID-19)-related articles. Methods: We conducted a rapid scoping review of HPSR papers published between 2010 and 2022 (inclusively) in the EMR, addressing the following areas: reporting of funding in HPSR papers, source of funding in the published HPSR papers, authors' affiliations and country of focus. We followed the Joanna Briggs Institute (JBI) guidelines for conducting scoping reviews. We also conducted univariate and bivariate analyses for all variables at 0.05 significance level. Results: Of 10,797 articles screened, 3408 were included (of which 9.3% were COVID-19-related). More than half of the included articles originated from three EMR countries: Iran (n = 1018, 29.9%), the Kingdom of Saudi Arabia (n = 595, 17.5%) and Pakistan (n = 360, 10.6%). Approximately 30% of the included articles did not report any details on study funding. Among articles that reported funding (n = 1346, 39.5%), analysis of funding sources across all country income groups revealed that the most prominent source was national (55.4%), followed by international (41.7%) and lastly regional sources (3%). Among the national funding sources, universities accounted for 76.8%, while governments accounted for 14.9%. Further analysis of funding sources by country income group showed that, in low-income and lower-middle-income countries, all or the majority of funding came from international sources, while in high-income and upper-middle-income countries, national funding sources, mainly universities, were the primary sources of funding. The majority of funded articles' first authors were affiliated with academia/university, while a minority were affiliated with government, healthcare organizations or intergovernmental organizations. We identified the following characteristics to be significantly associated with the funding source: country income level, the focus of HPSR articles (within the EMR only, or extending beyond the EMR as part of international research consortia), and the first author's affiliation. Similar funding patterns were observed for COVID-19-related HPSR articles, with national funding sources (78.95%), mainly universities, comprising the main source of funding. In contrast, international funding sources decreased to 15.8%. Conclusion: This is the first study to address the reporting of funding and funding sources in published HPSR articles in the EMR. Approximately 30% of HPSR articles did not report on the funding source. Study findings revealed heavy reliance on universities and international funding sources with minimal role of national governments and regional entities in funding HPSR articles in the EMR. We provide implications for policy and practice to enhance the profile of HPSR in the region. [ABSTRACT FROM AUTHOR]

Several research papers have attempted to describe the dynamics of COVID-19 based on systems of differential equations. These systems have taken into account quarantined or isolated cases, vaccinations, control measures, and demographic parameters, presenting propositions regarding theoretical results that often investigate the asymptotic behavior of the system. In this paper, we discuss issues that concern the theoretical results proposed in the paper "An Extended SEIR Model with Vaccination for Forecasting the COVID-19 Pandemic in Saudi Arabia Using an Ensemble Kalman Filter". We propose detailed explanations regarding the resolution of these issues. Additionally, this paper focuses on extending the local stability analysis of the disease-free equilibrium, as presented in the aforementioned paper, while emphasizing the derivation of theorems that validate the global stability of both epidemic equilibria. Emphasis is placed on the basic reproduction number R 0 , which determines the asymptotic behavior of the system. This index represents the expected number of secondary infections that are generated from an already infected case in a population where almost all individuals are susceptible. The derived propositions can inform health authorities about the long-term behavior of the phenomenon, potentially leading to more precise and efficient public measures. Finally, it is worth noting that the examined paper still presents an interesting epidemiological scheme, and the utilization of the Kalman filtering approach remains one of the state-of-the-art methods for modeling epidemic phenomena. [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 novel coronavirus disease (SARS-CoV-2) has caused many infections and deaths throughout the world; the spread of the coronavirus pandemic is still ongoing and continues to affect healthcare systems and economies of countries worldwide. Mathematical models are used in many applications for infectious diseases, including forecasting outbreaks and designing containment strategies. In this paper, we study two types of SIR and SEIR models for the coronavirus. This study focuses on the discrete-time and fractional-order of these models; we study the stability of the fixed points and orbits using the Jacobian matrix and the eigenvalues and eigenvectors of each case; moreover, we estimate the parameters of the two systems in fractional order. We present a statistical study of the coronavirus model in two countries: Saudi Arabia, which has successfully recovered from the SARS-CoV-2 pandemic, and China, where the number of infections remains significantly high. [ABSTRACT FROM AUTHOR]

Studying people's opinions is a growing research field. This area of research is known as sentiment analysis. The COVID-19 pandemic changed everything around the world and reduced social contact among people. Citizens and residents of Saudi Arabia experienced high stress during the pandemic, seeking entertainment via games and publishing their activities on social media platforms such as Twitter. In this paper, we focus on applying the Mazajak sentiment analyzer on tweets containing game keywords in Arabic collected using Twitter API during the lockdown period to decide whether users preferred playing individually or in groups. This can help designers and developers, as well as the Saudi General Entertainment Authority (GEA), focus on creating the most interesting games for individuals and improving them to meet users' expectations. Our approach has three main stages: tweet collection, tweet preparation, and finally, the application of sentiment analysis to get the desired goal based on people's behavior toward the games. The result of this paper confirms that people, in general, preferred playing in groups, instead of alone, during this period. [ABSTRACT FROM AUTHOR]

Sophia is the first social Humanoid to be entitled to a passport and citizenship of Saudi Arabia. This paradigm shifts in artificial intelligence from artificial machine learning to a socially personified humanoid is technologically a quantum leap that can, by several means, help the society. AI-powered robotics technology has proved its worth to fight against Covid19. This paper analyzes such cases where AI and Robotics have made the difference to facilitate the treatment process and has provided safe platforms and portals for public health management. Further, it emphasizes the ethical efficacy of AI-powered based humanoids systems which can save humans from the infection on the one hand and can offer medical services to treat the patients from Covid-19 pandemic like diseases in future. The paper makes a seminal contribution to developing medical ethics for AI-powered Robotics and Humanoids systems. [ABSTRACT FROM AUTHOR]

The paper endeavours to explore and analyse some critical issues in the oil and gas market that cropped up around the spread of COVID-19 and tries to identify the key drivers and triggers pertaining therewith. The spread of the first wave that began in March 2020 is crucial because of the global economic downturn that ensued due to lockdown and imposed restrictions coupled with a protracted oil price war that began between Saudi Arabia and Russia. The paper tries to address some key research questions to understand the triggers and drivers around the pandemic. These are: (1) whether the behaviour of OPEC or its key players around the pandemic could be considered uniquely different; (2) what could the triggers be for the increased volatilities that cropped up in both physical and financial markets during the pandemic; (3) what was really different about the oil market crisis around the pandemic that transformed it to an unprecedented storage crisis; (4) what really went wrong with the much-hyped U.S. shale boom during the pandemic that led to the bankruptcy of several oil and gas companies, followed by huge job losses. The paper relies on a structured review of relevant secondary literature to address these exploratory questions and builds upon a retrospective rumination on the world oil market from 1960 to 2020. This is complemented by an analysis of supporting data and evidence obtained from various sources. Considering the intertwining of oil and financial markets around the pandemic, the lessons and findings from the paper would not only be highly relevant for policymakers and stakeholders in the oil and gas sector but would be equally relevant for those in the financial markets. [ABSTRACT FROM AUTHOR]

As the number of COVID-19 cases is rapidly increasing internationally, management, recommendations and guidelines of COVID-19 are rapidly evolving and changing. Formulating local clinical management policies among institutions adopting these recommendations is vital to staff as well as the patients' safety. Also, training multidisciplinary teams on these policies is an important, yet challenging, part of the process. The purpose of this paper is to present the process that has been followed to formulate COVID-19-specific response anesthesiology and operating room (OR) policies at King Abdulaziz University Hospital, by applying David A. Kolb's experiential learning theory during simulation-based training. This project had a total of six simulation-based sessions (four simulation scenarios and two clinical drills) designed to test the efficacy and efficiency of the then current practice in the hospital, facing the COVID-19 pandemic. Qualitative data analysis was completed using qualitative thematic data analysis. To apply experiential Kolb's theory, session's checklist (two raters per session), outcomes, and participants' feedback to develop and improve clinical management pathway in the department were used. The 12 reports and participants' feedback highlighted three main areas for improvement. These are Personal Protective Equipment implementation, team dynamics, and airway management. This process then guided in creating a new understanding of the multidisciplinary clinical management pathway, in addition to enhancing viability of the current practice and clinical management guidelines and protocols, which were already established and adapted at the hospital before the COVID-19 pandemic crisis. The alignment with Kolb's experiential theory helped formulate anesthesiology and OR effective clinical management pathway has been demonstrated. Applying experiential learning theory by a clinical institute using interprofessional, multidisciplinary simulations and clinical drills can guide the process of formulating clinical management pathways during pandemic outbreaks. [ABSTRACT FROM AUTHOR]

Epidemiological models play pivotal roles in predicting, anticipating, understanding, and controlling present and future epidemics. The dynamics of infectious diseases is complex, and therefore, researchers need to consider more complicated mathematical models. In this paper, we first describe the dynamics of a complex SIR epidemic model with nonstandard nonlinear incidence and recovery rates. In this model, we consider the rate at which individuals lose immunity. Rigorous mathematical results have been established from the point of view of stability and bifurcation. The basic reproduction number ( R 0 ) is determined. We then apply LaSalle's invariance principle and Lyapunov's direct method to prove that the disease-free equilibrium is globally asymptotically stable when R 0 < 1. The model has a unique endemic equilibrium when R 0 > 1. A nonlinear Lyapunov function is used together with LaSalle's invariance principle to show that the endemic equilibrium is globally asymptotically stable under some conditions. Further, for the case when R 0 = 1 , we analyze the model and show a backward bifurcation under certain conditions. In the second part of this paper, we analyze a modified SIR model with a vaccination term, which must be a function of time. We show that the modified model agrees well with COVID-19 data in Saudi Arabia. We then investigate different future scenarios. Simulation results suggest that a two-pronged strategy is crucial to control the COVID-19 pandemic in Saudi Arabia. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease (COVID-19) pandemic has had devastating effects on healthcare systems and the global economy. Moreover, coronavirus has been found in human feces, sewage, and in wastewater treatment plants. In this paper, we highlight the transmission behavior, occurrence, and persistence of the virus in sewage and wastewater treatment plants. Our approach follows the process of identifying a coronavirus hotspot through existing wastewater plants in major cities of Saudi Arabia. The mathematical distributions, including the log-normal distribution, Gaussian model, and susceptible-exposed-infected-recovered (SEIR) model, are adopted to predict the coronavirus load in wastewater plants. We highlight not only the potential virus removal techniques from wastewater treatment plants but also methods of tracing SARS-CoV-2 in humans through wastewater treatment plants. The results indicate that our modeling approach may facilitate the analysis of SARS-CoV-2 loads in wastewater for early prediction of the epidemic outbreak and provide significant implications to the public health system. [ABSTRACT FROM AUTHOR]

This paper aims to examine the influence of different message framings (utilitarian, deontological, religious, virtue-based message, and God's punishment-based messages) on Saudi Arabians’ beliefs and behavioral intentions related to COVID-19 and the influence of message source (religious advocate, Saudi COVID-19 monitoring committee member, close person, physician, journalist, and social media influencer) on communicating messages in the COVID-19 pandemic context. The between-subject design experiment (n =222) was conducted online due to the Covid-19 restrictions in force at the time of this study and in an attempt to derive a representative sample from the general Saudi population. The results showed that the God’s punishment- based message was less effective than other moral and religious messages, including the non-framed messages, and member of the Saudi COVID-19 monitoring committee, followed by physicians, were believed to be the most effective message sources. Overall, the current research contributes to the knowledge about health and crisis communications in the collectivistic cultural context. [ABSTRACT FROM AUTHOR]

The paper summarizes the arguments and counterarguments within the scientific discussion on the impact of the COVID-19 pandemic on marketing. The main purpose of the research is to analyze how COVID-19 has affected the critical messaging strategies in the Saudi food industry. Systematization of scientific background on the investigated issue indicated that pandemics and epidemics significantly impact business processes, including marketing activities. The motivation behind this study is derived from several observations on how companies have adapted their business goals to contribute to tackling the impact of COVID-19. With changes in consumers' behavior, companies had to readjust their strategies to address the anticipated short- and long-term changes in market dynamics. The research aims to reveal trends in marketing communication in the pre-COVID-19 and post-COVID-19 eras. Therefore, the process of investigation of the topic in the paper is carried out using quantitative approaches. The article specifically focuses on the objectives such as 1) establishing how Shawarmer SA, Maestro Pizza, and Hungerstation interacted with their customer before and after the onset of the COVID-19 pandemic; 2) uncovering changes in social media messaging strategies used by Shawarmer SA, Maestro Pizza, and Hungerstation. Specifically, online survey on three major companies in Saudi Arabia, Shawarmer SA, Maestro Pizza, and Hungerstation. The paper presents the results of an empirical analysis of data from each of the above company's Twitter pages, which showed that the COVID-19 pandemic has significantly changed how companies interact with their customers. Therefore, the author sought to identify the appropriate strategies adopted by the three major players in the Saudi food industry, Maestro Pizza, Hunger Station, and Shawarmer, to interact with customers. The research empirically confirmed and theoretically proved that messaging is a crucial aspect of marketing. The results of the study can be helpful in the development of customer-oriented marketing strategies. [ABSTRACT FROM AUTHOR]

Objective. Internet of Things (IoT) integrates several technologies where devices learn from the experience of each other thereby reducing human-intervened likely errors. Modern technologies like IoT and machine learning enable the conventional to patient-specific approach transition in healthcare. In conventional approach, the biggest challenge faced by healthcare professionals is to predict a disease by observing the symptoms, monitoring the remote area patient, and also attending to the patient all the time after being hospitalised. IoT provides real-time data, makes decision-making smarter, and provides far superior analytics, and all these to help improve the quality of healthcare. The main objective of the work was to create an IoT-based automated system using machine learning models for symptom-based COVID-19 prognosis. Methods. Comparative analysis of predictive microbiology of COVID-19 from case symptoms using various machine learning classifiers like logistics regression, k-nearest neighbor, support vector machine, random forest, decision trees, Naïve Bayes, and gradient booster is reported here. For the sake of the validation and verification of the models, performance of each model based on the retrieved cloud-stored data was measured for accuracy. Results. From the accuracy plot, it was concluded that k-NN was more accurate (97.97%) followed by decision tree (97.79), support vector machine (97.42), logistics regression (96.50), random forest (90.66), gradient boosting classifier (87.77), and Naïve Bayes (73.50) in COVID-19 prognosis. Conclusion. The paper presents a health monitoring IoT framework having high clinical significance in real-time and remote healthcare monitoring. The findings reported here and the lessons learnt shall enable the healthcare system worldwide to counter not only this ongoing COVID but many other such global pandemics the humanity may suffer from time to come. [ABSTRACT FROM AUTHOR]

The transmission rate of COVID-19 varies over time. There are many reasons underlying this mechanism, such as seasonal changes, lockdowns, social distancing, and wearing face masks. Hence, it is very difficult to directly measure the transmission rate. The main task of the present paper was to identify the variable transmission rate ( β 1 ) for a SIR-like model. For this, we first propose a new compartmental forced SEYNHRV-S differential model. We then drive the nonlinear differential equation and present the finite difference technique to obtain the time-dependent transmission rate directly from COVID-19 data. Following this, we show that the transmission rate can be represented as a linear combination of radial kernels, where several forms of radial kernels are explored. The proposed model is flexible and general, so it can be adapted to monitor various epidemic scenarios in various countries. Hence, the model may be of interest for policymakers as a tool to evaluate different possible future scenarios. Numerical simulations are presented to validate the prediction of our SEYNHRV and forced SEYNHRV-S models, where the data from confirmed COVID-19 cases reported by the Ministry of Health in Saudi Arabia were used. These confirmed cases show the second wave of the infected population in Saudi Arabia. By using the COVID-19 data, we show that our model (forced SEYNHRV-S) is able to predict the second wave of infection in the population in Saudi Arabia. It is well known that COVID-19 epidemic data cannot be accurately represented by any compartmental approach with constant parameters, and this is also true for our SEYNHRV model. [ABSTRACT FROM AUTHOR]

Coronavirus, a pandemic threat to human beings, has awakened huge concern worldwide, but no bibliometric studies have been conducted on Coronavirus research during 2011-Feb.2020. The aim of this study is to map research productivity on the Coronavirus based on the articles indexed in the Scopus database. The articles related to Coronavirus dated from 2011 to Feb. 2020 were retrieved from the Scopus database. A total of 7558 papers were retrieved for this study which indexed by the Scopus database during 2010-Feb. 2020. The most productive year is recognized 2015with 1012 (13.38 %) articles while Maximum AGR 40.38 percent was recorded in the year. The USA ranked first with 2373 publications, followed by China with1378 publications while The Journal of Virology published the highest 330 articles followed by PLoS One with 224 articles. Dusten, C. is ranked topmost productive corresponding author (first position) with 108 articles followed by Memish, Z.A. with 107 articles. Medicine subject identified as the top subject for publishing paper on Coronavirus followed by Immunology and Microbiology. The title Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia received the highest 1346 citations, followed by Immune Signaling by RIG-I-like Receptors with 818 citations. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic spread rapidly worldwide. On September 15, 2021, a total of 546,251 confirmed cases were recorded in Saudi Arabia alone. Saudi Arabia imposed various levels of lockdown and forced the community to implement social distancing. In this paper, we formulate a mathematical model to study the impact of these measures on COVID-19 spread. The model is analyzed qualitatively, producing two equilibrium points. The existence and stability of the COVID-19 free equilibrium and the endemic equilibrium depend on the control reproduction number, Rc. These results are in good agreement with the numerical experiments. Moreover, the model is fitted with actual data from the COVID-19 dashboard of the Saudi Ministry of Health. We divide the timeline from March 12, 2020, to September 23, 2020, into seven phases according to the varied applications of lockdown and social distancing. We then explore several scenarios to investigate the optimal application of these measures and address whether it is possible to rely solely on social distancing without imposing a lockdown. [ABSTRACT FROM AUTHOR]

The pandemic of Covid-19 at the global level cannot be underestimated. Arab world including Saudi Arabia is affected with the pandemic of Covid-19. More importantly, the surge of the numbers of infected individuals in Iran has brought fear and anxiety in the whole region in general and Saudi Arabia in particular. Several studies have investigated the prevalence of the virus in different countries from various perspectives. Nonetheless, little focus is given to academic research on health challenge of the virus resulting to fear and anxiety in the recent time in the context of Saudi Arabia. This study primarily aims at exploring the health effect of Covid-19 in Saudi Arabia despite the fact that, the World Health Organization (WHO) has clamored for preventive measures in addressing global health effect of the virus. The methodology of the study is systematic literature review (SLR) and content analysis of the secondary data on health impact of Covid-19 and preventive measures against its transmission in the country are critically analyzed. The results of secondary data of the study indicated that a number of confirmed cases, active cases and deaths in Saudi Arabia manifested negative impact of Covid-19 on citizens' health especially fear and anxiety despite the fact that records have showed steady recovered cases in the country. More specifically, the findings indicated that, health impact of Covid-19 are as follows namely: fear or anxiety, negative attitude among health workers and women in health workforce. Similarly, Saudi Arabia has put in place various preventive measures such as: preventive measures against transmission of Covid-19 which are as follows: institutional preparedness and strengthening mechanism, curfews and lockdowns and suspension of activities and order of social distancing. In conclusion, this paper has lucidly highlighted the health effect of Covid-19 and safety measures against the transmission of Covid-19 have been elaborated in the context of Saudi Arabia. It is thereby suggested that, the outcome of this study can be helpful to the health workers and policymakers in the country to be acquainted with the magnanimity of the novel coronavirus as well as putting effective safety mechanisms in place in curtailing the spread of Covid-19 in Saudi Arabia. [ABSTRACT FROM AUTHOR]

Introduction: This paper aims to measure the performance of early detection methods, which are usually used for infectious diseases. Methodology: By using real data of confirmed Coronavirus cases from the Kingdom of Saudi Arabia and Italy, the moving epidemic method (MEM) and the moving average cumulative sums (Mov. Avg Cusum) methods are used in our simulation study. Results: Our results suggested that the CUSUM method outperforms the MEM in detecting the start of the Coronavirus outbreak. [ABSTRACT FROM AUTHOR]

• A fractional-order model for simulating the spread of COVID-19 is proposed. • Impact of governmental action and public perception. • Time-varying transmission rate based on the actions and perceptions. • Simulations using generalized exponential time-differencing (GETD). • Improved data fitting using fractional-orders rather than integer order. COVID-19 pandemic has impacted people all across the world. As a result, there has been a collective effort to monitor, predict, and control the spread of this disease. Among this effort is the development of mathematical models that could capture accurately the available data and simulate closely the futuristic scenarios. In this paper, a fractional-order memory-dependent model for simulating the spread of COVID-19 is proposed. In this model, the impact of governmental interventions and public perception are incorporated as part of the nonlinear time-varying transmission rate. In addition, an algorithm for approximating the optimal values of the fractional order and strength of governmental interventions is provided. This approach makes our model suitable for capturing the given data set and consequently reliable for future predictions. The model simulation is performed using the two-step generalized exponential time-differencing method and tested for data from Mainland China, Italy, Saudi Arabia and Brazil. The simulation results demonstrate that the fractional order model calibrates to the data better than its integer order counterpart. This observation is further endorsed by the calculated error metrics. [ABSTRACT FROM AUTHOR]

Background & Objective: The massive burden of COVID-19 disease is believed to be associated with significant psychological disturbances on healthcare professionals. Increasing levels of stress may lead to anxiety, depression, burnout syndrome, and in severe cases, post-traumatic stress disorder. The objective of this study was to examine the presence of stress among resident trainee doctors working in a tertiary care hospital in Saudi Arabia in the context of the corona pandemic. Methodology: This was a cross-sectional study conducted in Security Forces Hospital, Riyadh, Saudi Arabia. We included the resident trainee doctors from different specialties of our hospital including medical, surgical, anesthesia, emergency room (ER), and critical care units. We used convenience sampling to select study respondents. Perceived Stress Scale (PSS) was used for assessing the stress levels among trainees. Trainees were requested to fill the questionnaire either online survey monkey or in paper form. All data were entered and analyzed using SPSS version 26. We calculated mean, standard deviation, frequency count, and percentages for the perceived stress. Results: A total of 60 trainees were included in this study. Mean PSS-10 score was 20 ± 5.51, with a range between 6 and 34. Four (6.7 %) trainees had low stress and 50 (83.3 %) trainees had moderate stress while 6 (10 %) trainees had high stress Conclusion: The majority of resident trainees in our study experienced a moderate level of anxiety during this COVID pandemic. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic is spreading around the world becoming thus a serious concern for health, economic and social systems worldwide. In such situation, predicting as accurately as possible the future dynamics of the virus is a challenging problem for scientists and decision-makers. In this paper, four phenomenological epidemic models as well as Suspected-Infected-Recovered (SIR) model are investigated for predicting the cumulative number of infected cases in Saudi Arabia in addition to the probable end-date of the outbreak. The prediction problem is formulated as an optimization framework and solved using a Particle Swarm Optimization (PSO) algorithm. The Generalized Richards Model (GRM) has been found to be the best one in achieving two objectives: first, fitting the collected data (covering 223 days between March 2nd and October 10, 2020) with the lowest mean absolute percentage error (MAPE = 3.2889%), the highest coefficient of determination (R2 = 0.9953) and the lowest root mean squared error (RMSE = 8827); and second, predicting a probable end date found to be around the end of December 2020 with a projected number of 378,299 at the end of the outbreak. The obtained results may help the decision-makers to take suitable decisions related to the pandemic mitigation and containment and provide clear understanding of the virus dynamics in Saudi Arabia. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic continues to disrupt many health- care settings worldwide including cancer care. COVID-19 has been associated with worse outcomes amongst cancer patients. Saudi Arabia has experienced several Middle East respiratory syndrome coronavirus (MERS-CoV) outbreaks that affected the continuity of cancer care. In this paper, we describe how Saudi Arabia responded to COVID-19, how cancer care was re-restructured during this pandemic and how the recent MERS- CoV experience may have improved the Saudi response to COVID-19. [ABSTRACT FROM AUTHOR]

During the current COVID-19 global pandemic, the major efforts are channeled toward containing and minimizing the spread and maintaining the healthcare providers' safety. One of the major aspects of effective infection control and prevention is healthcare team training and system troubleshooting. Simulation-based education appears to be a practical and flexible instructional design to achieve variable levels of knowledge, skills, and attitude training. In this paper, we aim is to provide a brief scheme on how simulation-based training can be employed in COVID-19 pandemic preparedness efforts. In addition, we will be sharing our multidisciplinary simulation experience in critical care at the National Guard Health Affairs, Saudi Arabia. [ABSTRACT FROM AUTHOR]

Background: Like many international universities and institutions of higher education that have shifted the entire educational system from the traditional face-to-face education model to distance learning during the COVID-19 pandemic, universities in Saudi Arabia have expended great effort to change the behavior of educators and students to accommodate the transition. This rapid shift has had a significant impact on students, especially in programs such as interior design, in which different practical courses are taught along with the theoretical ones. Therefore, this study aimed to investigate the benefits and challenges senior interior design students experienced while taking their classes online. Design and Methods: The current paper has adopted a qualitative approach for assessing the data obtained. Semi-structured interview was used to collect all the needed information regarding participants' opinions about distance learning. Results: Increased time to spend on assignments, reduction of costs to the student, and improvement in achievement of learning outcomes were the three main benefits students reported in their experience of distance learning. On the other hand, technical issues, changes in grade distribution, and changes in project types were identified as student concerns. Although students found many advantages in communicating with others remotely, some issues arose. Conclusions: Students expressed a preference for the hybrid learning method, or at least for having their exams, juries, and presentations on campus in person, not remotely. [ABSTRACT FROM AUTHOR]

The article discusses the Annals of Thoracic Medicine, Alhamad et al. reported the characteristics and outcomes of idiopathic pulmonary fibrosis patients seen and evaluated at tertiary care interstitial lung disease center. Topics include the authors of paper have to commended for applying the definition of Group 3 pulmonary hypertension; and another notable feature of paper has the reported incidence of acute exacerbations.

To mitigate the spread of the Novel Coronavirus (COVID-19), governments around the world have imposed social distancing policies ranging from minor social activity suspensions to full curfews. These social distancing policies have altered electricity consumption behaviors in numerous countries. Many governments imposed strict social distancing policies during a temperature transition season where the impacts of temperature variations are particularly important for the operation of the electric grid. This paper studies how strict social distancing policies affect the relationship between electricity demand and ambient temperature. We first review the expected short- and long-term impacts of social distancing on the electricity demand. We then present a case study on the electricity demand of the Kingdom of Saudi Arabia during strict social distancing policies. The results of this case study suggest that strict social distancing policies result in a stronger correlation between temperature and electricity demand compared to previous years. Additionally, we observe a reduction in the time required for the electricity demand to respond to temperature changes. Power system regulators can use the results in this paper to better design energy policies. The results can also be used by power system operators to more accurately forecast electricity demands and avoid inefficient and insecure operation of the electric grid. [ABSTRACT FROM AUTHOR]

This paper proposes, validates and analyzes the dynamics of the susceptible exposed infectious recovered (SEIR) model for the propagation of COVID-19 in Saudi Arabia, which recorded the largest number of cases in the Arab world. The model incorporates a saturated incidence rate, a constant vaccination rate and a nonlinear treatment function. The rate of treatment is assumed to be proportional to the number of infected persons when this number is low and reaches a fixed value for large number of infected individuals. The expression of the basic reproduction number is derived, and the model basic stability properties are studied. We show that when the basic reproduction number is less than one the model can predict both a Hopf and backward bifurcations. Simulations are also provided to fit the model to COVID-19 data in Saudi Arabia and to study the effects of the parameters of the treatment function and vaccination rate on disease control. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has not only caused a global health crisis, but it has also had significant environmental and human consequences. During the COVID-19 pandemic, this study focused on emerging challenges in managing healthy personal protective materials (HPPM) in Kingdom of Saudi Arabia, using silty sand (SM) soil as an example since it covers large areas in KSA and in the whole world. The main objective of this paper is to find a novel way to minimize pandemic-related waste by using HPPM as waste materials in road construction. For the first time, a series of experiments was conducted on a mixture of different percentages of shredded HPPM (0, 0.5, 1 and 2%) added to the silty sand (SM) soil for road applications, including soil classification according to the USCS, modified compaction, UCS, UPV, and CBR. In addition, a numerical simulation was performed using geotechnical-based software Plaxis 3D to study the performance of the soil-HPPM mix as a subbase layer in the paving structure under heavy traffic loading. The modified compaction test results show that there is an increase in the optimum moisture content with increasing the HPPM contents from 0.5% to 1% and 2%. However, a reduction in the maximum dry density is observed. The values of dry density and water content at 0%, 0.5%, 1% and 2% pf HPPM are 2.045, 1.98, 1.86 and 1.8 g/cm3 and 7.65% 8%, 8.5% and 9.5%, respectively. The soaked CBR values at 0, 0.5, 1 and 2% HPPM are 23, 30, 8, 2% with the maximum value attained with the addition of 0.5% HPPM. The results of UCS were with the same percentages of HPPM 430, 450, 430 and 415 kPa, respectively, with the maximum value attained with 0.5% HPPM addition as well. In contrast, the values of UVP at 0%, 0.5%, 1% and 2% are 978.5, 680.3, 489.4 and 323.6 m/s, respectively, confirming the trends obtained by modified compaction test results. The simulation results confirm this conclusion that the soil-HPPM mix show a superior performance when used as a subbase layer and reduced vertical displacement by a percentage of 11% compared to the normal subbase material. By eliminating HPPM especially facemasks from the landfill lifecycle, incorporating them into high quality construction material production has the potential to deliver significant environmental benefits. [Display omitted] 1 Influence of polypropylene (HPPM) fibres on the performance of sandy soil is clearly studied. 2 The optimum fibres content is obtained as 0.5% volume fraction. 3 Positive improvement is also noticed in unconfined compressive strength and compaction after adding HPPM fibres. 4 Fibres play a major role in crack bridging. [ABSTRACT FROM AUTHOR]

Meteorological conditions may influence the incidence of many infectious diseases. Coronavirus disease-2019 (COVID-19) is a highly contagious, air-borne, emerging, viral disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). In 2020, the COVID-19 global pandemic affected more than 210 countries and territories worldwide including Saudi Arabia. There are contradictory research papers about the correlation between meteorological parameters and incidence of COVID-19 in some countries worldwide. The current study investigates the impact of outdoor and indoor meteorological conditions on the daily recorded COVID-19 cases in western region (Makkah and Madinah cities) of Saudi Arabia over a period of 8 months from March to October 2020. Reports of the daily confirmed COVID-19 cases from the webpage of Saudi Ministry of Health (MOH) were used. Considering, the incubation period of COVID-19 which ranged from 2 to 14 days, the relationships between daily COVID-19 cases and outdoor meteorological factors (temperature, relative humidity, and wind speed) using a lag time of 10 days are investigated. The results showed that the highest daily COVID-19 cases in Makkah and Madinah were reported during the hottest months of the year (April–July 2020) when outdoor temperature ranged from 26.51 to 40.71 °C in Makkah and of 23.89–41.20 °C in Madinah, respectively. Partial negative correlation was detected between outdoor relative humidity and daily recorded COVID-19 cases. No obvious correlation could be demonstrated between wind speed and daily COVID-19 cases. This indicated that most of SARS-CoV-2 infection occurred in the cool, air-conditioned, dry, and bad-ventilated indoor environment in the investigated cities. These results will help the epidemiologists to understand the correlation between both outdoor and indoor meteorological conditions and SARS-CoV-2 transmissibility. These findings would be also a useful supplement to assist the local healthcare policymakers to implement and apply a specific preventive measures and education programs for controlling of COVID-19 transmission. [Display omitted] • No significant correlation between outdoor temperature and daily reported COVID-19 cases in western region of Saudi Arabia. • Partial negative association of outdoor relative humidity with daily recorded COVID-19 cases. • Almost all COVID-19 cases result from indoor infection among the households. • Pre-symptomatic and asymptomatic infected individuals facilitate household infection spread. • Prevention of indoor infection assists COVID-19 control especially in tropical and hyper-arid countries. [ABSTRACT FROM AUTHOR]

In this paper, an extended SEIR model with a vaccination compartment is proposed to simulate the novel coronavirus disease (COVID-19) spread in Saudi Arabia. The model considers seven stages of infection: susceptible (S), exposed (E), infectious (I), quarantined (Q), recovered (R), deaths (D), and vaccinated (V). Initially, a mathematical analysis is carried out to illustrate the non-negativity, boundedness, epidemic equilibrium, existence, and uniqueness of the endemic equilibrium, and the basic reproduction number of the proposed model. Such numerical models can be, however, subject to various sources of uncertainties, due to an imperfect description of the biological processes governing the disease spread, which may strongly limit their forecasting skills. A data assimilation method, mainly, the ensemble Kalman filter (EnKF), is then used to constrain the model outputs and its parameters with available data. We conduct joint state-parameters estimation experiments assimilating daily data into the proposed model using the EnKF in order to enhance the model's forecasting skills. Starting from the estimated set of model parameters, we then conduct short-term predictions in order to assess the predicability range of the model. We apply the proposed assimilation system on real data sets from Saudi Arabia. The numerical results demonstrate the capability of the proposed model in achieving accurate prediction of the epidemic development up to two-week time scales. Finally, we investigate the effect of vaccination on the spread of the pandemic. [ABSTRACT FROM AUTHOR]

The wide spread of Covid-19 has led to infecting a huge number of patients, simultaneously. This resulted in a massive number of requests for medical care, at the same time. During the first wave of Covid-19, many people were not able to get admitted to appropriate hospitals because of the immense number of patients. Admitting patients to suitable hospitals can decrease the in-bed time of patients, which can lead to saving many lives. Also, optimizing the admission process can minimize the waiting time for medical care, which can save the lives of severe cases. The admission process needs to consider two main criteria: the admission time and the readiness of the hospital that will accept the patients. These two objectives convert the admission problem into a Multi-Objective Problem (MOP). Pareto Optimization (PO) is a common multi-objective optimization method that has been applied to different MOPs and showed its ability to solve them. In this paper, a PO-based algorithm is proposed to deal with admitting Covid-19 patients to hospitals. The method uses PO to vary among hospitals to choose the most suitable hospital for the patient with the least admission time. The method also considers patients with severe cases by admitting them to hospitals with the least admission time regardless of their readiness. The method has been tested over a real-life dataset that consisted of 254 patients obtained from King Faisal specialist hospital in Saudi Arabia. The method was compared with the lexicographic multi-objective optimization method regarding admission time and accuracy. The proposed method showed its superiority over the lexicographic method regarding the two criteria, which makes it a good candidate for real-life admission systems. [ABSTRACT FROM AUTHOR]

Under the influence of the COVID-19 pandemic and the concurrent oil conflict between Russia and Saudi Arabia, oil prices have exhibited unusual and sudden changes. For this reason, the volatilities of the West Texas Intermediate (WTI), Brent and Dubai crude daily oil price data between 29 May 2006 and 31 March 2020 are analysed. Firstly, the presence of chaotic and nonlinear behaviour in the oil prices during the pandemic and the concurrent conflict is investigated by using the Shanon Entropy and Lyapunov exponent tests. The tests show that the oil prices exhibit chaotic behavior. Additionally, the current paper proposes a new hybrid modelling technique derived from the LSTARGARCH (Logistic Smooth Transition Autoregressive Generalised Autoregressive Conditional Heteroskedasticity) model and LSTM (long-short term memory) method to analyse the volatility of oil prices. In the proposed LSTARGARCHLSTM method, GARCH modelling is applied to the crude oil prices in two regimes, where regime transitions are governed with an LSTAR-type smooth transition in both the conditional mean and the conditional variance. Separating the data into two regimes allows the efficient LSTM forecaster to adapt to and exploit the different statistical characteristics and ARCH and GARCH effects in each of the two regimes and yield better prediction performance over the case of its application to all the data. A comparison of our proposed method with the GARCH and LSTARGARCH methods for crude oil price data reveals that our proposed method achieves improved forecasting performance over the others in terms of RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) in the face of the chaotic structure of oil prices. [ABSTRACT FROM AUTHOR]