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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 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]

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]

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]

The purpose of this paper is to explore the impact of inventory turnover on the profitability level of Saudi manufacturers. The data comprises 78 manufacturers listed on the Saudi Stock Exchange and was used to test the research hypothesis. The related data over the 2017–2021 period were collected from annual reports and the Datastream database. After running a multiple regression analysis with a fixed effects model, findings showed that the higher the inventory turnover ratio, the higher the cost which could be suppressed, and the greater the profitability of a company. The outcomes of this study have significant implications for managerial accounting issues in the setting of Saudi Arabia. Further, they provide policy recommendations to decision makers and assist managers in enhancing sustainability in the manufacturing sector. This research is the first to investigate this relationship including the impact of COVID-19 among Saudi companies in several industries, thus filling a gap in comparable research. [ABSTRACT FROM AUTHOR]

Compatible with global sustainable development report, 2016 edition, and vision 2030, Saudi Arabia recognized the importance of technology in achieving the Sustainable Development Goals (SDGs). This paper aims to measure the self-perception of digital skills among students in Saudi Arabia's higher education system to understand how they were influenced before and amid the COVID-19 pandemic. In 2019 before the COVID-19 pandemic, we started a project to study the self-perception of digital skills among Saudi Arabia university students (group A). A total of 469 students participated in this research. The validity and reliability of the employed scale were tested with first-order confirmatory factor analysis (CFA). The differences between the two groups (before and amid the pandemic) were tested through the Mann-Whitney U test. The results for group A (N = 232 students) showed a higher self-perception of their digital skills. In March 2020, amid the pandemic, Saudi Arabia closed and shifted to technology-based teaching like many other countries worldwide. After students' return to universities in 2021, an evaluation of how the students perceived their own digital skills was again conducted (group B). The results for group B (N = 237 students) demonstrated a lower level of confidence in their own digital abilities. Comparing two groups (A and B), after the educational course was administered, group A (prior to COVID-19) had greater self-perceptions of digital skills than group B (amid COVID-19). Students' perceptions of their own digital skills have been negatively impacted as a result of the pandemic situation caused by COVID-19. The collected evidence suggests that there is a difference, and that this difference is statistically significant. As a result of the substantial relationship between self-perception of digital skills and how students deal with reality based on their own self-perception, Saudi Arabia higher education ministry shifted teaching methods to be based on technology. Other significant findings and their implications for practice and theory were reported in this study. Finally, limitations and prospects for future research were also elaborated.

The correct distribution of service facilities can help keep fixed and overhead costs low while increasing accessibility. When an appropriate location is chosen, public-sector facilities, such as COVID-19 centers, can save lives faster and provide high-quality service to the community at a low cost. The purpose of the research is to highlight the issues related to the location of COVID-19 vaccine centers in the city of Jeddah, Saudi Arabia. In particular, this paper aims to analyze the accessibility of COVID-19 vaccine centers in Jeddah city using maximal coverage location problems with and without constraint on the number and capacity of facilities. A maximal coverage model is first used to analyze the COVID-19 vaccination coverage of Jeddah districts with no restriction on the facility capacity. Then, a maximize capacitated coverage method is utilized to assess the centers' distribution and demand coverage with capacity constraints. Finally, the minimize facilities model is used to identify the most optimal location required to satisfy all demand points with the least number of facilities. The optimization approaches consider the objective function of minimizing the overall transportation time and travel distance to reduce wastage on the service rate provided to the patients. The optimization model is applied to a real-world case study in the context of the COVID-19 vaccination center in Jeddah. The results of this study provide valuable information that can help decision-makers locate and relocate COVID-19 centers more effectively under different constraints conditions.

The pandemic's context is rife with numerous dangerous threats and high fear levels, influencing human decision-making. Such characteristics are identified by investigating the acceptance of exposure detection apps from the technology acceptance model (TAM) perspective. This study purposed a model to investigate protection technology acceptance, specifically exposure detection apps in the context of COVID-19. Quantitative study approach and a cross-section design targeted 586 participants from Saudi Arabia. As the study model is complex, the study hypotheses were analysed using the structural equation modelling-partial least squares (SEM-PLS3) approach. The findings support the entire model hypothesis except the link between social media awareness and exposure detection apps' intention. Mediation of COVID-19 anxiety and influence was confirmed as well. The current paper contributes to the technologies acceptance domain by developing a context-driven model comprising the major pandemic characteristics that lead to various patterns of technology acceptance. This study also fills the literature gap regarding mediating effects of social influence and COVID-19 anxiety in the relationship between trust in government and exposure detection apps implementation, and between COVID-19 anxiety and exposure detection apps implementation, respectively. The results may assist government agencies, health policymakers, and health organisations in the wide world and specifically Saudi Arabia, in their attempts to contain the COVID-19 pandemic spread.

In the recent past, the COVID-19 epidemic has impeded global economic progress and, by extension, all of society. This type of pandemic has spread rapidly, posing a threat to human lives and the economy. Because of the growing scale of COVID-19 cases, employing artificial intelligence for future prediction purposes during this pandemic is crucial. Consequently, the major objective of this research paper is to compare various deep learning forecasting algorithms, including auto-regressive integrated moving average, long short-term memory, and conventional neural network techniques to forecast how COVID-19 would spread in Saudi Arabia in terms of the number of people infected, the number of deaths, and the number of recovered cases. Three different time horizons were used for COVID-19 predictions: short-term forecasting, medium-term forecasting, and long-term forecasting. Data pre-processing and feature extraction steps were performed as an integral part of the analysis work. Six performance measures were applied for comparing the efficacy of the developed models. LSTM and CNN algorithms have shown superior predictive precision with errors of less than 5% measured on available real data sets. The best model to predict the confirmed death cases is LSTM, which has better RMSE and R 2 values. Still, CNN has a similar comparative performance to LSTM. LSTM unexpectedly performed badly when predicting the recovered cases, with RMSE and R 2 values of 641.3 and 0.313, respectively. This work helps decisionmakers and health authorities reasonably evaluate the status of the pandemic in the country and act accordingly. [ABSTRACT FROM AUTHOR]

Purpose: This paper studies a simple SVIR (susceptible, vaccinated, infected, recovered) type of model to investigate the coronavirus's dynamics in Saudi Arabia with the recent cases of the coronavirus. Our purpose is to investigate coronavirus cases in Saudi Arabia and to predict the early eliminations as well as future case predictions. The impact of vaccinations on COVID-19 is also analyzed. Methods: We consider the recently introduced fractional derivative known as the generalized Hattaf fractional derivative to extend our COVID-19 model. To obtain the fitted and estimated values of the parameters, we consider the nonlinear least square fitting method. We present the numerical scheme using the newly introduced fractional operator for the graphical solution of the generalized fractional differential equation in the sense of the Hattaf fractional derivative. Mathematical as well as numerical aspects of the model are investigated. Results: The local stability of the model at disease-free equilibrium is shown. Further, we consider real cases from Saudi Arabia since 1 May–4 August 2022, to parameterize the model and obtain the basic reproduction number R 0 v ≈ 2.92 . Further, we find the equilibrium point of the endemic state and observe the possibility of the backward bifurcation for the model and present their results. We present the global stability of the model at the endemic case, which we found to be globally asymptotically stable when R 0 v > 1 . Conclusion: The simulation results using the recently introduced scheme are obtained and discussed in detail. We present graphical results with different fractional orders and found that when the order is decreased, the number of cases decreases. The sensitive parameters indicate that future infected cases decrease faster if face masks, social distancing, vaccination, etc., are effective. [ABSTRACT FROM AUTHOR]

Background: Intussusception (ISN) post-COVID-19 infection in children is rare but can occur. SARS-CoV-2 may play a role in the pathogenesis of ISN and trigger immune activation and mesenteric adenitis, which predispose peristaltic activity to "telescope" a proximal bowel segment into the distal bowel lumen. Objectives: To estimate the prevalence of SARS-CoV-2 infection in ISN children and analyze the demographic parameters, clinical characteristics and treatment outcomes in ISN pediatric patients with COVID-19 illness. Methods: We performed this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Studies reporting on the incidence of ISN post-SARS-CoV-2 infection in children, published from 1 December 2019 until 1 October 2022, in PROQUEST, MEDLINE, EMBASE, PUBMED, CINAHL, WILEY ONLINE LIBRARY, SCOPUS and NATURE, with a restriction to articles available in the English language, were included. Results: Of the 169 papers that were identified, 34 articles were included in the systematic review and meta-analysis (28 case report, 5 cohort and 1 case-series studies). Studies involving 64 ISN patients with confirmed COVID-19 (all patients were children) were analyzed. The overall pooled proportions of the ISN patients who had PCR-confirmed SARS-CoV-2 infection was 0.06% (95% CI 0.03 to 0.09, n = 1790, four studies, I2 0%, p = 0.64), while 0.07% (95% CI 0.03 to 0.12, n = 1552, three studies, I2 0%, p = 0.47) had success to ISN pneumatic, hydrostatic and surgical reduction treatment and 0.04% (95% CI 0.00 to 0.09, n = 923, two studies, I2 0%, p = 0.97) had failure to ISN pneumatic, hydrostatic and surgical reduction treatment. The median patient age ranged from 1 to 132 months across studies, and most of the patients were in the 1–12 month age group (n = 32, 50%), p = 0.001. The majority of the patients were male (n = 41, 64.1%, p = 0.000) and belonged to White (Caucasian) (n = 25, 39.1%), Hispanic (n = 13, 20.3%) and Asian (n = 5, 7.8%) ethnicity, p = 0.000. The reported ISN classifications by location were mostly ileocolic (n = 35, 54.7%), and few children experienced ileo-ileal ISN (n = 4, 6.2%), p = 0.001. The most common symptoms from ISN were vomiting (n = 36, 56.2%), abdominal pain (n = 29, 45.3%), red currant jelly stools (n = 25, 39.1%) and blood in stool (n = 15, 23.4%). Half of the patients never had any medical comorbidities (n = 32, 50%), p = 0.036. The approaches and treatments commonly used to manage ISN included surgical reduction of the ISN (n = 17, 26.6%), pneumatic reduction of the ISN (n = 13, 20.2%), antibiotics (n = 12, 18.7%), hydrostatic reduction of the ISN (n = 11, 17.2%), laparotomy (n = 10, 15.6%), intravenous fluids (n = 8, 12.5%) and surgical resection (n = 5, 7.8%), p = 0.051. ISN was recurrent in two cases only (n = 2, 3.1%). The patients experienced failure to pneumatic (n = 7, 10.9%), hydrostatic (n = 6, 9.4%) and surgical (n = 1, 1.5%) ISN treatment, p = 0.002. The odds ratios of death were significantly higher in patients with a female gender (OR 1.13, 95% CI 0.31–0.79, p = 0.045), Asian ethnicity (OR 0.38, 95% CI 0.28–0.48, p < 0.001), failure to pneumatic or surgical ISN reduction treatment (OR 0.11, 95% CI 0.05–0.21, p = 0.036), admission to ICU (OR 0.71, 95% CI 0.83–1.18, p = 0.03), intubation and placement of mechanical ventilation (OR 0.68, 95% CI 0.51–1.41, p = 0.01) or suffering from ARDS (OR 0.88, 95% CI 0.93–1.88, p = 0.01) compared to those who survived. Conclusion: Children with SARS-CoV-2 infection are at low risk to develop ISN. A female gender, Asian ethnicity, failure to ISN reduction treatment (pneumatic or surgical), admission to ICU, mechanical ventilation and suffering from ARDS were significantly associated with death following ISN in pediatric COVID-19 patients. [ABSTRACT FROM AUTHOR]

The first case of coronavirus disease 2019 (COVID-19) in Saudi Arabia was reported on 2 March 2020. Since then, it has progressed rapidly and the number of cases has grown exponentially, reaching 788,294 cases on 22 June 2022. Accurately analyzing and predicting the spread of new COVID-19 cases is critical to develop a framework for universal pandemic preparedness as well as mitigating the disease's spread. To this end, the main aim of this paper is first to analyze the historical data of the disease gathered from 2 March 2020 to 20 June 2022 and second to use the collected data for forecasting the trajectory of COVID-19 in order to construct robust and accurate models. To the best of our knowledge, this study is the first that analyzes the outbreak of COVID-19 in Saudi Arabia for a long period (more than two years). To achieve this study aim, two techniques from the data analytics field, namely the auto-regressive integrated moving average (ARIMA) statistical technique and Prophet Facebook machine learning technique were investigated for predicting daily new infections, recoveries and deaths. Based on forecasting performance metrics, both models were found to be accurate and robust in forecasting the time series of COVID-19 in Saudi Arabia for the considered period (the coefficient of determination for example was in all cases more than 0.96) with a small superiority of the ARIMA model in terms of the forecasting ability and of Prophet in terms of simplicity and a few hyper-parameters. The findings of this study have yielded a realistic picture of the disease direction and provide useful insights for decision makers so as to be prepared for the future evolution of the pandemic. In addition, the results of this study have shown positive healthcare implications of the Saudi experience in fighting the disease and the relative efficiency of the taken measures. [ABSTRACT FROM AUTHOR]

In this paper, we measured the impact of a full COVID-19 lockdown on ophthalmic patients after a period of lockdown in Saudi Arabia, from March to September 2020. A cross-sectional analytical study was carried out on 180 patients who had their appointments delayed or canceled due to the lockdown. Data was collected from electronic medical records and patients via voice calls using a validated questionnaire that were analyzed using a multivariable binary regression analysis. The results show no statistically significant mean difference in visual acuity when comparing pre- and post-lockdown measurements. The median number of appointment cancellations/delays per patient was two, and the estimated delay for the first canceled appointments was equal to 178.8 days. Of the cohort studied, 15.4% of patients faced delays in necessary surgical and therapeutic interventions; 22.1% of patients sought eye care at other institutions due to the delay, and 15% of those were seen by doctors unspecialized in ophthalmology. The odds of dissatisfaction with care were higher in patients who experienced cancellations in a surgical procedure and patients who experienced difficulty in obtaining medications. In conclusion, the pandemic hampered ophthalmic patients' access to medications. Subjective visual outcomes of patients were also negatively affected; however, the change in objective visual parameters was not statistically significant. [ABSTRACT FROM AUTHOR]

The use of information technology and technological medical devices has contributed significantly to the transformation of healthcare. Despite that, many problems have arisen in diagnosing or predicting diseases, either as a result of human errors or lack of accuracy of measurements. Therefore, this paper aims to provide an integrated health monitoring system to measure vital parameters and diagnose or predict disease. Through this work, the percentage of various gases in the blood through breathing is determined, vital parameters are measured and their effect on feelings is analyzed. A supervised learning model is configured to predict and diagnose based on biometric measurements. All results were compared with the results of the Omron device as a reference device. The results proved that the proposed design overcame many problems as it contributed to expanding the database of vital parameters and providing analysis on the effect of emotions on vital indicators. The accuracy of the measurements also reached 98.8% and the accuracy of diagnosing COVID-19 was 64%. The work also presents a user interface model for clinicians as well as for smartphones using the Internet of things. [ABSTRACT FROM AUTHOR]

To reduce the spread of COVID-19, lockdowns were implemented in almost every single country in the world including Saudi Arabia. In this paper, the effect of COVID-19 lockdown on O3, NO2, and PM10 in Makkah was analysed using air quality and meteorology data from five sites. Two approaches were employed: (a) comparing raw measured concentrations for the lockdown period in 2019 and 2020; and (b) comparing weather-corrected concentrations estimated by the machine learning approach with observed concentrations during the lockdown period. According to the first approach, the average levels of PM10 and NO2 decreased by 12% and 58.66%, respectively, whereas the levels of O3 increased by 68.67%. According to the second approach, O3 levels increased by 21.96%, while the levels of NO2 and PM10 decreased by 13.40% and 9.66%, respectively. The machine learning approach after removing the effect of changes in weather conditions demonstrated relatively less reductions in the levels of NO2 and PM10 and a smaller increase in the levels of O3. This showed the importance of adjusting air pollutant levels for meteorological conditions. O3 levels increased due to its inverse correlation with NO2, which decreased during the lockdown period. [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]

COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic's path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, simple exponential smoothing Holt, and HoltWinters. The models were built using publicly available daily data of COVID-19 during the period of 24 March 2020 to 5 April 2021 reported in Saudi Arabia. The experimental results indicate that the ARIMA model had a smaller prediction error in forecasting confirmed cases, which is consistent with results reported in the literature, while cubic spline showed better predictions for recoveries and deaths. As more data become available, a fluctuation in the forecasting-accuracy metrics was observed, possibly due to abrupt changes in the data.

This paper explores the influence of crisis-related information on customers’ recommendations and purchase intentions. Various studies have investigated the role of risk-related information in terms of its certainty and immediacy. This research introduces a model that differentiates and investigates different types of information provided by organizations through social network sites (SNSs) that are related to the coronavirus (COVID-19) crisis, including situational information and precautionary measures information specifically. The proposed model targeted users of SNSs in Saudi Arabia, where around 72.38% of its population is considered active SNSs users with 3 h of average daily time spent on SNSs. Based on the survey data collected from 356 online shoppers in Saudi Arabia, structural equation modeling (SEM) was applied to test the proposed model. The reliability of instrument was tested using Confirmatory Factor Analysis (CFA), where the results showed acceptable model fit indices (X²/df = 2.6, GFI = 0.93, CFI = 0.94, TLI = 0.93, and RMSEA = 0.067). Based on that, the structural model was built and tested, which also showed acceptable fit indices (X²/df = 2.9, GFI = 0.92, CFI = 0.93, TLI = 0.91, and RMSEA = 0.073). The findings highlight that situational and adopted precautionary measures information had a significant impact on recommendations (γ=0.73) and purchase intentions (γ=0.39). Further, organizations that hold themselves accountable for broadcasting updated information related to the crisis and disseminating the necessary preventive measures develop a high level of recommendations and purchase intentions among customers. A personal recommendation construct was confirmed as a partial mediator, where an indirect significant influence was found of broadcasting information on purchase intention through personal recommendation (γ=0.309). Therefore, this paper suggests that organizations should consider offering customers all the necessary types of information related to the COVID-19 crisis. [ABSTRACT FROM AUTHOR]

In this work, we developed, validated, and analysed the behaviour of a compartmental model of COVID-19 transmission in Saudi Arabia. The population was structured into four classes: susceptible (S), exposed (E), infectious (I), and removed (R) individuals. This SEIR model assumes a bilinear incidence rate and a nonlinear recovery rate that depends on the quality of health services. The model also considers a treatment function and incorporates the effect of fear due to the disease. We derived the expression of the basic reproduction number and the equilibrium points of the model and demonstrated that when the reproduction number is less than one, the disease-free equilibrium is stable, and the model predicts a backward bifurcation. We further found that when the reproduction number is larger than one, the model predicts stable periodic behaviour. Finally, we used numerical simulations with parameter values fitted to Saudi Arabia to analyse the effects of the model parameters on the model-predicted dynamic behaviours. [ABSTRACT FROM AUTHOR]

Various published COVID-19 models have been used in epidemiological studies and healthcare planning to model and predict the spread of the disease and appropriately realign health measures and priorities given the resource limitations in the field of healthcare. However, a significant issue arises when these models need help identifying the distribution of the constituent variants of COVID-19 infections. The emergence of such a challenge means that, given limited healthcare resources, health planning would be ineffective and cost lives. This work presents a universal neural network (NN) computational instrument for predicting the mainstream symptomatic infection rate of COVID-19 and models of the distribution of its associated variants. The NN is based on a mixture density network (MDN) with a Gaussian mixture model (GMM) object as a backbone. Twelve use cases were used to demonstrate the validity and reliability of the proposed MDN. The use cases included COVID-19 data for Canada and Saudi Arabia, two date ranges (300 and 500 days), two input data modes, and three activation functions, each with different implementations of the batch size and epoch value. This array of scenarios provided an opportunity to investigate the impacts of epistemic uncertainty (EU) and aleatoric uncertainty (AU) on the prediction model's fitting. The model accuracy readings were in the high nineties based on a tolerance margin of 0.0125. The primary outcome of this work indicates that this easy-to-use universal MDN helps provide reliable predictions of COVID-19 variant distributions and the corresponding synthesized profile of the mainstream infection rate. [ABSTRACT FROM AUTHOR]

Background: According to prevailing views in neuroscience, near-death experiences (NDE) occurring after severe head trauma, critical illness, or coma are often life-transforming experiences in which no awareness or sensory experience of any kind is possible. Although there are general patterns, each case is quite different from the other and requires accurate recording and reporting to potentially explain the phenomenon. Aim: This narrative study aimed to explore a pregnant woman's NDE due to complications from MERS-CoV. Methods: This was a qualitative narrative study with the administration of two unstructured interviews. After the second interview, the participant completed the Greyson NDE scale, presented through descriptive statistics. Qualitative data were analyzed using Labov's model of narrative analysis through abstract, orientation, complicating action, evaluation, resolution, and coda. Results: The Greyson scale resulted in a total score of 12, confirming that the patient had experienced an NDE. Labov's model of narrative analysis revealed that the patient's experience was not limited to the NDE but had implications for her recovery and life. The patient experienced all three types of NDEs: out-of-body, transcendental, including the transition of consciousness to another dimension, and a combined experience. She also suffered from prolonged hallucinations, neuropathy, and post-intensive care syndrome (PICS). At the same time, the patient experienced what is known as NDE aftereffects, which are caused by a change in beliefs and values; she began to lead a more altruistic life and became interested in the meaning of life. Conclusions: NDE survivors should be encouraged to talk more and share their stories with others if they wish. This study not only investigates the NDE but also considerably adds to the existing literature by integrating a unique cultural view from a country outside of the US and other Western nations, and it highlights the significant role of healthcare providers in NDEs and the importance of communication with comatose patients. It underscores the need for compassion when dealing with patients with NDEs. [ABSTRACT FROM AUTHOR]

Background and Objectives: In 2020, one of the most important steps that were made was to give priority to the development of a COVID-19 vaccine to prevent the rising incidence of COVID-19 from continuing to rise. However, globally, there is a variable acceptance of the level of the COVID-19 vaccine. This study aims to explore Saudi parents' willingness to vaccinate their children against COVID-19. Materials and Methods: This was a cross-sectional study; the online questionnaire was used to investigate the views of Saudi parents with children aged under 18 toward the immunization of their children against COVID-19. The data were gathered from 10 May 2022 to 31 October 2022. The data analysis uses SPSS version 20. A p-value of 0.05 or lower was regarded as statistically significant. Results: A total of 978 Saudi parents participated in this study. Most of the respondents were from the age group of 36–45 years with the educational qualification of high school and bachelor's degree. Overall, it was observed that the majority, 98.2% of the respondents, disclosed that they needed more information (p = 0.004) about COVID-19 vaccine safety among children so that they could decide whether to vaccinate their child. About 91.4% of parents mention that vaccination against COVID-19 is not necessary for children (p = 0.001). About 68.3% of respondents agreed that getting vaccinated against COVID-19 could help Saudi Arabia control COVID-19 (p = 0.007, RI = 0.76). In terms of negative attitudes toward COVID-19 vaccination, 71.3% think that there will be severe side effects (p = 0.019, RI = 0.75); 67.7% think that the vaccine's protection will only last for a short time (p = 0.055, RI = 0.72); 80.1% said they were afraid of getting vaccinated because of needle fear (p = 0.045, RI = 0.76), and 41.2% said lack of time was the main barrier to not vaccinating their child. Conclusions: Parents expressed concerns regarding the safety and efficacy of the COVID-19 vaccine, which might be some of the main factors influencing their decision to vaccinate their children. It is the need of the hour to take action to communicate, educate, and intervene with Saudi parents to enhance COVID-19 vaccination compliance rates across the board. [ABSTRACT FROM AUTHOR]

The inappropriate prescribing of antimicrobials increases antimicrobial resistance (AMR), which poses an appreciable threat to public health, increasing morbidity and mortality. Inappropriate antimicrobial prescribing includes their prescribing in patients hospitalized with COVID-19, despite limited evidence of bacterial infections or coinfections. Knowledge of current antimicrobial utilization in Saudi Arabia is currently limited. Consequently, the objective of this study was to document current antimicrobial prescribing patterns among Saudi hospitals during the COVID-19 pandemic. This study included patients with or without COVID-19 who were admitted to five hospitals in Makkah, Saudi Arabia. Data were gathered using the Global PPS methodology and analyzed using descriptive statistics. Out of 897 hospitalized patients, 518 were treated with antibiotics (57.7%), with an average of 1.9 antibiotics per patient. There were 174 culture reports collected, representing 36.5% of all cases. The most common indication for antibiotics use was community-acquired infections, accounting for 61.4% of all cases. 'Watch' antibiotics were the most commonly prescribed antibiotics, with the cephalosporins and carbapenems representing 38.7% of all antibiotics prescribed, followed by the penicillins (23.2%). Notably, Piperacillin/Tazobactam and Azithromycin were prescribed at relatively higher rates for COVID-19 patients. These findings highlight the need for continuous efforts to optimize the rational use of antibiotics through instigating appropriate antimicrobial stewardship programs in hospitals and, as a result, reduce AMR in the country. [ABSTRACT FROM AUTHOR]

(1) Background: Vaccination is a cornerstone of public health strategy for mitigating the morbidity and mortality associated with seasonal influenza. However, vaccine hesitancy and misconceptions pose significant barriers to this effort, particularly in the context of the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to investigate the transfer of COVID-19 vaccine hesitancy to the influenza vaccine and to identify misconceptions about the influenza vaccine among the Saudi population in the post-COVID-19 era. (2) Methods: A web-based, cross-sectional study was conducted between February and June 2023 using a questionnaire adapted from the Adult Vaccine Hesitancy Scale (aVHS). The questionnaire was disseminated to 589 Saudi residents, aged 18 and above, with access to digital devices. Data were analyzed via logistic regression analysis to determine the associations between vaccine hesitancy, knowledge of influenza, and baseline characteristics. (3) Results: This study found that 37.7% of respondents exhibited vaccine hesitancy, while 56.7% demonstrated good knowledge about influenza. There was a significant relationship between nationality and vaccine hesitancy (p-value > 0.05), with non-Saudi respondents exhibiting higher hesitancy. Logistic regression analysis revealed significant associations between vaccine hesitancy, age, and nationality. Meanwhile, participants with higher educational qualifications showed greater knowledge about influenza. (4) Conclusions: The findings highlight an important crossover of COVID-19 vaccine hesitancy to influenza vaccines. This study underscores the need for targeted public health interventions to address misconceptions about the influenza vaccine, particularly among certain demographic groups, in order to improve influenza vaccine uptake in the post-COVID era. [ABSTRACT FROM AUTHOR]

Background: Vaccine hesitancy has been identified by the World Health Organization (WHO) as a major worldwide health threat. Home Health Care (HHC) service recipients represent a vulnerable group and were prioritized to receive coronavirus disease (COVID-19) vaccination during the national vaccine campaigns in Saudi Arabia. We aimed to investigate the most frequent reasons for vaccine hesitancy among home health care recipients in Saudi Arabia. Methods: This cross-sectional survey was conducted among home health care (HHC) service recipients in Saudi Arabia from February 2022 to September 2022. The behavioral and social drivers (BeSD) model developed by the WHO was used to understand the factors affecting vaccination decision making in our cohort. Results: Of the 426 HHC service recipients enrolled in the study, a third were hesitant to complete the COVID-19 vaccination series. The most prevalent reported reason for COVID-19 vaccine refusal was concerns about the vaccine side effects (41.6%). Factors independently associated with COVID-19 vaccination hesitancy were: having chronic conditions (odds ratio [OR] = 2.59; 95% confidence interval [CI] = 1.33–5.05, p = 0.005), previous COVID-19 diagnosis (OR = 0.48; 95% CI: 0.28–0.82, p = 0.008), ease of getting the COVID-19 vaccine by themselves (OR = 0.49; 95% CI: 0.28–0.89, p = 0.018), belief in the importance of COVID-19 vaccine in protecting their health (OR = 0.60; 95% CI: 0.38–0.96, p = 0.032), and confidence in the safety of COVID-19 vaccination (OR = 0.38; 95% CI: 0.21–0.69, p = 0.001). Conclusion: Only one-third of the study participants were hesitant to complete the series of COVID-19 vaccination. Understanding the factors underpinning vaccine hesitancy among this group would help healthcare workers and policymakers in developing personalized health awareness campaigns aimed at improving vaccine acceptance levels. [ABSTRACT FROM AUTHOR]

This study contributes to the ongoing debate on the size effect and size-based investment styles by investigating the return and volatility spillovers and time-varying conditional correlations among Saudi large-, mid-, and small-cap indices. To this end, we utilize the weekly returns on the MSCI Saudi large-, mid-, and small-cap indices over a long sample period, spanning several crises. The econometric approach that we use is a VAR-asymmetric BEKK-GARCH model which accounts for structural breaks. On the basis of the VAR-asymmetric BEKK-GARCH model estimation results, we calculate portfolio weights and hedge ratios, and discuss their risk management implications. The empirical results confirm the presence of unilateral return spillovers running from mid- to small-cap stocks, while multilateral volatility spillovers are documented, albeit substantially weakened when accounting for structural breaks. The time-varying conditional correlations display clear spikes around crises, which translate to higher hedge ratios, increasing the cost of hedging during turbulent times. The optimal portfolio weights suggest that investors generally overweight large caps in their portfolios during uncertain times to minimize risk without lowering expected returns. The main takeaway from our results is that passively confining fund managers to a particular size category regardless of the prevailing market conditions may lead to suboptimal performance. [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]

COVID-19 poses a significant burden to healthcare systems. Healthcare organisations with better health innovation infrastructures have faced a reduced burden and achieved success in curbing COVID-19. In Saudi Arabia, digital technologies have played a vital role in fighting SARS-CoV-2 transmission. In this paper, we aimed to summarise the experience of optimising digital health technologies in Saudi Arabia as well as to discuss capabilities and opportunities during and beyond the COVID-19 pandemic. A literature search was conducted up to September 2021 to document the experience of using DHTPs in Saudi Arabia in response to the COVID-19 outbreak. We also considered any published data, press briefings, and announcements by the MOH in Saudi Arabia. The findings were synthesised in narrative form. Health officials succeeded in optimising and maintaining a strategy to mitigate the spread of the virus via different digital technologies, such as mobile health applications, artificial intelligence, and machine learning. The quick digital response in Saudi Arabia was facilitated by governmental support and by considering users and technology determinants. Future research must concentrate on establishing and updating the guidelines for using DHTPs. [ABSTRACT FROM AUTHOR]

The financial system in the Kingdom of Saudi Arabia (KSA) has a history of relative soundness, particularly in banking, due to comparatively strict and enforced domestic supervision, and supported by what has been for the most part a reasonably robust economy. However, the sector is facing challenges of a sluggish economy, need for reform and negative effects of the COVID-19 pandemic. This paper strives to assess how well the government thus far has responded to the challenges in the financial sector. The working hypothesis is that the insurance industry has improved its position, resulting in higher efficiency and profitability and lower risk. This is an industry historically plagued by too many players, financial issues and less than adequate controls. The analysis undertaken bears out the hypothesis, as revealed by the enhanced contribution made by the industry since the pandemic. Analysis suggests that strides have been made in that industry in terms of helping to diversify the economy with the onset of the pandemic. Moreover, evidence is provided that the sensitivity to changes in oil volumes (rather than to changes in oil prices) is a key risk factor for the financial sector in the KSA. These findings have implications for policy makers in terms of leveraging the pandemic conditions as an opportunity to drive further reform and diversify the economy with lower risk. [ABSTRACT FROM AUTHOR]

Hajj pilgrimage is a large mass gathering global event that may facilitate the spread and emergence of various infectious diseases as well as antimicrobial resistance (AMR) in a local and global scenario. Planning and preparing for these public health issues is a challenging and complex process for the Kingdom of Saudi Arabia (KSA) health authorities. Despite multiple efforts for the prevention and treatment of infectious diseases through longtime funding in education and medical care, the prevalence of infectious disease is still high among Hajj pilgrims. The commonly observed infectious diseases during Hajj include respiratory tract infections (influenza and pneumonia), urinary tract infections and skin infections that may necessitate the use of antimicrobials. Beta-lactams are used as a first-line treatment for hospital acquired infections as well as community acquired infections due to their broad-spectrum activity. However, most of the bacterial isolates such as Staphylococcus spp., Pseudomonas spp. and E. coli are resistant to beta-lactams. Irrational use of antimicrobials, lack of infection prevention practices and suboptimal healthcare access further exacerbate the risk of spreading AMR among Hajj pilgrims. Enhanced collaboration between countries, sharing of best practices and international cooperation are crucial in addressing AMR threats among pilgrims. Consequently, robust surveillance systems for early detection and monitoring of AMR, collaboration with national as well as international healthcare agencies, effective infection prevention and control measures, public awareness and rational use of antimicrobials via antimicrobial stewardship programs are required to mitigate the risk of AMR and ensure the health and well-being of pilgrims during Hajj. [ABSTRACT FROM AUTHOR]

COVID-19 is associated with a common symptom of olfactory dysfunction, which may persist even after the infection is resolved. Olfactory training (OT) has emerged as the most effective intervention for post-viral olfactory dysfunction. OT involves daily exposure of the olfactory system to various odors. The current study aims to explore olfactory dysfunction following COVID-19 and the potential benefits of olfactory training. Methods: This is a cross-sectional study conducted among adults aged 18–60 living in Alahssa, Saudi Arabia. An online questionnaire containing an informed consent form and a survey to collect demographic data, vaccination status, level of loss of smell and taste, and the level of awareness about olfactory training (OT) was distributed among all participants who agreed to participate in this study. Results: The study included 524 participants and presented their baseline characteristics, including age, gender, COVID-19 infection status, and complaints. Most patients were female (66.0%), and 46.2% had previously been infected with COVID-19. About 54.8% of participants reported chemosensory dysfunction, while 286 had olfactory dysfunction. Of those, 29.8% had anosmia, 16.8% had hyposmia, and 8.0% had parosmia. Results suggest that being fully or partially vaccinated may offer some protection against olfactory dysfunction compared to being unvaccinated. Adherence to olfactory training was associated with improvement in the sense of smell. Conclusions: The study highlights the importance of awareness and adherence to olfactory training, which may improve the sense of smell in individuals with chemosensory dysfunction. The findings of this study can inform public health policies and interventions aimed at reducing the burden of olfactory dysfunction associated with COVID-19 vaccination. [ABSTRACT FROM AUTHOR]

COVID-19 is a highly contagious disease caused by SARS-CoV-2. Vaccination against the virus was first approved in Saudi Arabia in December 2020. Vaccinated individuals are still at risk of getting infected with the virus and can transmit the disease. Therefore, the perception of vaccinated individuals regarding the disease can help limit the spread of the virus. Objectives: To measure the risk perception of COVID-19 following vaccination and factors that have an effect on risk perception; to identify the health protective behaviours of the vaccinated individuals. Methodology: This is a quantitative analytical cross-sectional, questionnaire-based study. The target population includes individuals aged 18 and above who live in the Riyadh region and have been vaccinated, during the period of June 2021 to December 2021. Results: The perception of 30.2% of participants did not change after vaccination, with many participants continuing to "always" take precautions even after vaccination. Numerous factors, such as age, gender, marital status, occupational status, employment status, and total household income, have shown significant effects towards risk perception. Conclusion: Many vaccinated individuals have continued to take precautionary steps and their risk perception has not changed. [ABSTRACT FROM AUTHOR]

The coronavirus disease of 2019 (COVID-19) pandemic has resulted in an unprecedented circumstance that has never previously occurred. This has caused the Saudi Arabian people to recognize the necessity of preventive measures and explore alternative systems, such as using natural products (NPs), for treating their infection. Therefore, the specific objectives of this study were to explore the factors that influence the selection of NPs for COVID-19 management and to know the outcome of using NPs in COVID-19 infection management. This observational cross-sectional study was conducted in Saudi Arabia between February and April 2022. The validated pretested questionnaire was distributed among different regions of the country via a purposive snowball sampling procedure. Both descriptive statistics and stepwise regression analyses were carried out to evaluate the parameters related to the use of medicinal plants for the prevention of COVID-19 and the treatment of respiratory symptoms during the pandemic. The data obtained were statistically analyzed using IBM SPSS Statistics for Windows, version 25 (IBM Corp., Armonk, NY, USA). Of the 677 participants, 65% reported using NPs for themselves or family members during COVID-19. Utilizing NPs is always given priority by a significant (p < 0.001) percentage of survey respondents. Further, a highly significant (p < 0.001) percentage of participants felt that using NPs reduced their COVID-19 symptoms without having any remarkable (p < 0.001) adverse effects. Family and friends (59%) were the most frequent sources of information about utilizing NPs, followed by personal experience (41%). Honey (62.7%) and ginger (53.8%) were the most utilized NP among participants. Moreover, black seeds, garlic and turmeric were used by 40.5%, 37.7% and 26.3% of the surveyors, respectively. Those who used NPs before COVID-19 were 72.9% more likely to use them during COVID-19. NPs are more likely to be used by 75% of people who live in the central part of the country and whose families prefer it. This is true even if other factors are considered, such as the practice of using NPs along with traditional therapies and the fact that some participants' families prefer it. Our findings show that NPs were commonly used to treat COVID-19 infection among Saudi Arabian residents. Close friends and family members mainly encouraged the use of NPs. Overall, the use of NPs was high among those who participated in our study; such practices are strongly impacted by society. It is essential to promote extensive studies to improve the recognition and accessibility of these products. Authorities should also educate the people about the benefits and risks of using commonly used NPs, especially those reported in this study. [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]

This study examines food safety awareness and practices among handlers in the food service sector at Riyadh City hospitals during the COVID-19 pandemic. Three hundred and fifteen (315) food service workers completed the entire questionnaire from five hospitals in Riyadh City between December 2020 and February 2021. The contributor's respondents' three-part questionnaire was divided according to general characteristics, food safety awareness, and food safety practices. The findings show that food handlers demonstrated good knowledge, techniques, and attitudes regarding maintaining food quality and ensuring food safety. Moreover, a significant positive correlation between food safety awareness and food safety practices was observed. Nevertheless, the correlation between the food handler's knowledge and safe food handling was negative. In general, our findings revealed the significance of education and the regular training of food service staff to improve learning and ensure better and safer food-handling practices, which could contribute to applying food safety practices in hospitals. [ABSTRACT FROM AUTHOR]

This systematic review synthesizes the findings of quantitative studies examining the relationships between Health Belief Model (HBM) constructs and COVID-19 vaccination intention. We searched PubMed, Medline, CINAHL, Web of Science, and Scopus using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and identified 109 eligible studies. The overall vaccination intention rate was 68.19%. Perceived benefits, perceived barriers, and cues to action were the three most frequently demonstrated predictors of vaccination intention for both primary series and booster vaccines. For booster doses, the influence of susceptibility slightly increased, but the impact of severity, self-efficacy, and cues to action on vaccination intention declined. The impact of susceptibility increased, but severity's effect declined sharply from 2020 to 2022. The influence of barriers slightly declined from 2020 to 2021, but it skyrocketed in 2022. Conversely, the role of self-efficacy dipped in 2022. Susceptibility, severity, and barriers were dominant predictors in Saudi Arabia, but self-efficacy and cues to action had weaker effects in the USA. Susceptibility and severity had a lower impact on students, especially in North America, and barriers had a lower impact on health care workers. However, cues to action and self-efficacy had a dominant influence among parents. The most prevalent modifying variables were age, gender, education, income, and occupation. The results show that HBM is useful in predicting vaccine intention. [ABSTRACT FROM AUTHOR]

Seeking an alternative approach for detecting adverse drug reactions (ADRs) in coronavirus patients (COVID-19) and enhancing drug safety, a retrospective study of six months was conducted utilizing an electronic medical record (EMR) database to detect ADRs in hospitalized patients for COVID-19, using "ADR prompt indicators" (APIs). Consequently, confirmed ADRs were subjected to multifaceted analyses, such as demographic attribution, relationship with specific drugs and implication for organs and systems of the body, incidence rate, type, severity, and preventability of ADR. The incidence rate of ADRs is 37%, the predisposition of organs and systems to ADR is observed remarkably in the hepatobiliary and gastrointestinal systems at 41.8% vs. 36.2%, p < 0.0001, and the classes of drugs implicated in the ADRs are lopinavir-ritonavir 16.3%, antibiotics 24.1%, and hydroxychloroquine12.8%. Furthermore, the duration of hospitalization and polypharmacy are significantly higher in patients with ADRs at 14.13 ± 7.87 versus 9.55 ± 7.90, p < 0.001, and 9.74 ± 5.51 versus 6.98 ± 4.36, p < 0.0001, respectively. Comorbidities are detected in 42.5% of patients and 75.2%, of patients with DM, and HTN, displaying significant ADRs, p-value < 0.05. This is a symbolic study providing a comprehensive acquaintance of the importance of APIs in detecting hospitalized ADRs, revealing increased detection rates and robust assertive values with insignificant costs, incorporating the hospital EMR database, and enhancing transparency and time effectiveness. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 has become a global health threat resulting in a catastrophic spread and more than 3.8 million deaths worldwide. It has been suggested that there is a negative influence of diabetes mellites (DM), which is a complex chronic disease, on COVID-19 severe outcomes. Other factors in diabetic patients may also contribute to COVID-19 disease outcomes, such as older age, obesity, hyperglycaemia, hypertension, and other chronic conditions. Methods: A cohort study was conducted on the demographics, clinical information, and laboratory findings of the hospitalised COVID-19 with DM and non-DM patients were obtained from the medical records in King Faisal Specialist Hospital and Research Centre, Saudi Arabia. Results: Among the study population, 108 patients had DM, and 433 were non-DM patients. Patients with DM were more likely to present symptoms such as fever (50.48%), anorexia (19.51%), dry cough (47.96%), shortness of breath (35.29%), chest pain (16.49%), and other symptoms. There was a significant decrease in the mean of haematological and biochemical parameters, such as haemoglobin, calcium, and alkaline phosphate in people with diabetes compared to non-diabetics and a considerable increase in other parameters, such as glucose, potassium, and cardiac troponin. Conclusions: According to the findings of this study, patients who have diabetes have a greater risk of developing more severe symptoms associated with COVID-19 disease. This could result in more patients being admitted to the intensive care unit as well as higher mortality rates. [ABSTRACT FROM AUTHOR]

The World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) a pandemic on 11 March 2020. The evolution of this pandemic has raised global health concerns, making people worry about how to protect themselves and their families. This has greatly impacted people's sentiments. There was a dire need to investigate a large amount of social data such as tweets and others that emerged during the post-pandemic era for the assessment of people's sentiments. As a result, this study aims at Arabic tweet-based sentiment analysis considering the COVID-19 pandemic in Saudi Arabia. The datasets have been collected in two different periods in three major regions in Saudi Arabia, which are: Riyadh, Dammam, and Jeddah. Tweets were annotated with three sentiments: positive, negative, and neutral after due pre-processing. Convolutional neural networks (CNN) and bi-directional long short memory (BiLSTM) deep learning algorithms were applied for classifying the sentiment of Arabic tweets. This experiment showed that the performance of CNN achieved 92.80% accuracy. The performance of BiLSTM was scored at 91.99% in terms of accuracy. Moreover, as an outcome of this study, an overwhelming upsurge in negative sentiments were observed in the dataset during COVID-19 compared to the negative sentiments of the dataset before COVID-19. The technique has been compared with the state-of-the-art techniques in the literature and it was observed that the proposed technique is promising in terms of various performance parameters. [ABSTRACT FROM AUTHOR]

This work presents a new polymorphic, reusable, and comprehensive mathematical model for COVID-19 epidemic transition cycle dynamics. This model has the following characteristics: (1) The core SEIR model includes asymptomatic and symptomatic infections; (2) the symptomatic infection is a multi-variant; (3) the recovery stage provides a partial feed to the symptomatic infection; and (4) the symptomatic and asymptomatic stages have additional feed streams from the protected stage. The proposed formalisation template is a canonical way to achieve different models for the underlying health control environment. This template approach endows the model with polymorphic and reusable capability across different scenarios. To verify the model's reliability and validity, this work utilised two sets of initial conditions: date range and COVID-19 data for Canada and Saudi Arabia. [ABSTRACT FROM AUTHOR]

In 2020, coronavirus (COVID-19) was declared a global pandemic and it remains prevalent today. A necessity to model the transmission of the virus has emerged as a result of COVID-19's exceedingly contagious characteristics and its rapid propagation throughout the world. Assessing the incidence of infection could enable policymakers to identify measures to halt the pandemic and gauge the required capacity of healthcare centers. Therefore, modeling the susceptibility, exposure, infection, and recovery in relation to the COVID-19 pandemic is crucial for the adoption of interventions by regulatory authorities. Fundamental factors, such as the infection rate, mortality rate, and recovery rate, must be considered in order to accurately represent the behavior of the pandemic using mathematical models. The difficulty in creating a mathematical model is in identifying the real model variables. Parameters might vary significantly across models, which can result in variations in the simulation results because projections primarily rely on a particular dataset. The purpose of this work was to establish a susceptible–exposed–infected–recovered (SEIR) model describing the propagation of the COVID-19 outbreak throughout the Kingdom of Saudi Arabia (KSA). The goal of this study was to derive the essential COVID-19 epidemiological factors from actual data. System dynamics modeling and design of experiment approaches were used to determine the most appropriate combination of epidemiological parameters and the influence of COVID-19. This study investigates how epidemiological variables such as seasonal amplitude, social awareness impact, and waning time can be adapted to correctly estimate COVID-19 scenarios such as the number of infected persons on a daily basis in KSA. This model can also be utilized to ascertain how stress (or hospital capacity) affects the percentage of hospitalizations and the number of deaths. Additionally, the results of this study can be used to establish policies or strategies for monitoring or restricting COVID-19 in Saudi Arabia. [ABSTRACT FROM AUTHOR]

Fear of infection has been sparked by the advent of the novel coronavirus disease (COVID-19). Insomnia in college students, especially its correlations and predictions with mental diseases, remains a research concern. Aim: To estimate the prevalence of fear related to COVID-19, depression, anxiety, and insomnia among female nursing college students throughout the pandemic and to determine the predictors of insomnia. Methods: A web-based cross-sectional descriptive study used 145 female nursing college students. Results: Students reported fear related to COVID-19, depression, and anxiety at rates of 79.3%, 30.2%, and 35.2%, respectively. Insomnia disturbed 24.7% of students. Anxiety predicted worsening insomnia in the student (AOR = 1.08, 95% CI: 0.92–0.97, p < 0.001). Fear related to COVID-19 was also a predictor (AOR = 0.96, 95% CI: 1.07–1.21, p < 0.05). Additionally, when depression severity declined, the chance of insomnia improved (AOR = 0.87, 95% CI: 0.85–0.91, p < 0.001). Insomnia was more common in chronically unwell students (AOR = 1.50, 95% CI = 1.01–2.24, p < 0.05). Conclusion: During the COVID-19 pandemic, university students' mental health should be monitored, and all essential safeguards should be taken, including resource allocation, awareness raising efforts, and the building of a mental health counseling facility. [ABSTRACT FROM AUTHOR]

Food and sleep are critical for human survival. However, for individuals with eating disorders, they face two critical disruptions in terms of abnormal feeding patterns as well as poor sleep. These difficulties are exacerbated as a result of the recent coronavirus pandemic, which caused drastic changes in daily life schedules and living arrangements. The current study therefore aims to explore, through qualitative means, how individuals with eating disorders are affected during the subsequent lockdowns, with a particular emphasis on the pandemic's impact on their sleep in Saudi Arabia. Two groups of participants were recruited: participants with eating disorders and healthy controls. Based on thematic analysis of focus group discussion data, it was found that both types of participants experienced poorer sleep and poorer mental health. Participants with eating disorders too showed a deterioration of their symptoms. However, healthy participants tended to show greater levels of recovery and coping. Based on the study findings, recommendations for future studies are made. [ABSTRACT FROM AUTHOR]

COVID-19 is a major global crisis affecter, changing global norms and societal behavioral models. Many companies have faced existential crises, but on the other hand, businesses that were and are helping others to boost digitalization, ICT and software solutions deployment, remote communications integration, e-commerce & e-services, and so on, have boosted their businesses, as people shifted online during the global lockdown and international travel restrictions. Our work explores the trend of e-commerce and e-services utilization during the ease of restrictions and the social distancing period to forecast the trend continuation patterns after the pandemic. An online survey was conducted and targeted individuals in Saudi Arabia and Egypt, resulting in 155 participants. The data were analyzed from four perspectives: demographics, COVID-19 health impact, trend analysis, and regression analysis. The results indicate heavy utilization of e-commerce and e-services during the global movement restrictions and travel bans. This trend has, however, significantly reduced during the ease of restrictions and social distancing period. Utilizing e-commerce and e-services in Saudi Arabia and Egypt, based on the research data, is positively correlated to the outbreak conditions. On the other hand, current data still does not give clear indications, and this pattern is going to be mostly, partly, or not at all permanent now as societies are returning to mostly a free movement of people and marginally restricted social distancing times. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) has significantly reshaped consumer behaviors in Saudi Arabia, as in most other countries worldwide, and it has played a critical role in rising commercial online activities. The purpose of this study is to test the factors affecting online shopping amid COVID-19 in Saudi Arabia. The five main factors identified from the literature review towards online shopping namely, product variety, convenience, payment method, trust, and psychological factors were analyzed and examined in the Saudi context. The research collected data online through a pre-tested instrument, which was directed to online Saudi consumers via different electronic tools, e.g., email and social media platforms. The results of a statistical analysis showed that only three factors have a direct significant impact on online shopping amid the COVID-19 pandemic. These factors were product variety, payment method, and psychological factors. Convenient and trust factors failed to have a significant impact on consumers' decisions to shop online amid COVID-19. Both factors were less important for consumers, since shopping online amid COVID-19 has become most common among people. The result will assist e-commerce businesses to better meet consumer demands by adjusting their marketing strategies, especially in times of crisis. [ABSTRACT FROM AUTHOR]

Antibiotic resistance is a global health and development threat, especially during the Severe Acute Respiratory Syndrome Coronavirus 2 (COVID-19) pandemic. Therefore, the current study was conducted to describe antibiotic usage and resistance among patients with COVID-19 in the intensive care unit (ICU) in Makkah, Saudi Arabia. In this cross-sectional study, only patients with positive COVID-19 status (42 patients) admitted to the ICU at the King Faisal Hospital were selected using a census sampling method. The susceptibility test of bacteria was carried out according to the standard protocol. The identified strains were tested in-vitro against several antibiotics drugs. Statistical analysis was performed using SPSS version 24. A total of 42 patients were included, with a mean age of 59.35 ± 18 years. Of them, 38.1% were males, and 61.9% were females. 35.7% have blood group O +. For age and blood groups, statistically significant associations were found between males and females, with p-values = 0.037 and 0.031, respectively. A large percentage (42.7%) of the obtained samples contained Klebsiella Pneumoniae; all bacteria were multidrug-resistance bacteria. Furthermore, 76.2% of bacteria were resistant to Ampicillin, 66.7% were resistant to Ciprofloxacin, 64.3% were resistant to Levofloxacin, 57.1% were resistant to Imipenem, and 57.1% were resistant to Moxifloxacin. On the contrary, among the 40 examined antibiotics, the effective antibiotics were Daptomycin, Linezolid, Mupirocin, Synercid, Teicoplanin, Vancomycin, and Nitrofurantoin. Our study demonstrates that antibiotic resistance is highly prevalent among ICU patients with COVID-19 at the King Faisal Hospital. Additionally, all bacteria were multidrug-resistance bacteria. Therefore, this high prevalence should be seriously discussed and urgently considered. [ABSTRACT FROM AUTHOR]

Being in a rapidly changing and dynamic environment during the COVID-19 pandemic, individuals' perceptions change on a daily basis, and this starts to inform the status of knowledge, attitude, and practices (KAP) during the second wave of the pandemic. Aim: To assess the KAP on COVID-19 among students, teaching staff, and administrative staff in the Asir region for the first time. Methods: A questionnaire-based survey cross-sectional study was conducted from 10 February to 10 March 2021 using an online questionnaire. Results: In the survey of students and staff in educational institutes in the Asir region, the target population were well informed about COVID-19; in 10 out of 13 questions, more than 80% of the respondents answered correctly. The attitude of the target populations was quite positive. They were worried about the increase in the number of COVID-19 cases. The target populations were concerned (94.8%) for their kith and kin not to get infected. The target populations agree with the precautionary measures that were taken by Saudi authorities. A positive association was found between knowledge and practices as well as knowledge and attitudes of the target population. A significantly positive association was observed between attitudes and practices of the educational institutes' respondents in Asir, Saudi Arabia. Conclusion: The students and staff in educational institutes in the Asir region have a high level of knowledge about COVID-19, have a positive attitude toward it, take proper precautions against it, and are enthusiastic about the COVID-19 vaccine. [ABSTRACT FROM AUTHOR]

Background: Various reports described new-onset diabetes during or after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in patients with no previous history of diabetes or glucocorticoid use. Further, SARS-CoV-2 could increase the risk of diabetes, including diabetic ketoacidosis (DKA). However, data on the relationship between new-onset diabetes and COVID-19 are still limited in our region. Thus, we aimed in this study to evaluate the association between new-onset diabetes and DKA in patients with COVID-19. Methods: A retrospective, cross-sectional study was conducted at a diabetic center in Jazan province, Saudi Arabia, between 2020 and 2021. Demographic data, COVID-19 status, and DKA incidence were collected and verified manually from diabetic patients' medical records. Data were analyzed using a t-test and chi-square test. Results: We included 54 diabetic patients diagnosed during the COVID-19 pandemic, with a median age of 17 years. The majority of patients were females (57.4%). About 38.8% were diagnosed with COVID-19, and 16.6% reported having DKA. About 33.3% of the patients who experienced DKA reported being COVID-19-positive. However, only 6% of patients who denied contracting SARS-CoV-2 developed DKA (p-value = 0.020). Conclusions: Patients with newly diagnosed diabetes due to COVID-19 seem at a higher risk of developing DKA. Further epidemiological and molecular studies are required for a better understanding of the correlation between DKA in patients with diabetes and COVID-19. [ABSTRACT FROM AUTHOR]

Natural symmetry exists in several phenomena in physics, chemistry, and biology. Incorporating these symmetries in the differential equations used to characterize these processes is thus a valid modeling assumption. The present study investigates COVID-19 infection through the stochastic model. We consider the real infection data of COVID-19 in Saudi Arabia and present its detailed mathematical results. We first present the existence and uniqueness of the deterministic model and later study the dynamical properties of the deterministic model and determine the global asymptotic stability of the system for R 0 ≤ 1 . We then study the dynamic properties of the stochastic model and present its global unique solution for the model. We further study the extinction of the stochastic model. Further, we use the nonlinear least-square fitting technique to fit the data to the model for the deterministic and stochastic case and the estimated basic reproduction number is R 0 ≈ 1.1367 . We show that the stochastic model provides a good fitting to the real data. We use the numerical approach to solve the stochastic system by presenting the results graphically. The sensitive parameters that significantly impact the model dynamics and reduce the number of infected cases in the future are shown graphically. [ABSTRACT FROM AUTHOR]