Purpose: Patient length of stay (LOS) is an important indicator of emergency department (ED) performance. Investigating factors that influence LOS could thus improve healthcare delivery and patient safety. Previous studies have focused on patient-level factors to explain LOS variation, with little research into service-related factors. This study examined the association between LOS and multi-level factors including patient-, service- and organization-level factors.Design/methodology/approach: This study uses a retrospective observational design to identify a cohort of patients from arrival to discharge from ED. A year-long data regarding patients flow trhoguh ED were analyzed using analytics techniques and multi-regression models. The response variable was patient LOS, and the independent variables were patient characteristics, service-related factors and organizational variables.Findings: The findings of this study showed that older patients, middle triage and hospitalization were all associated with longer LOS. Service-related factors such as complexity of care provided, initial ward designation and ward transfer had a significant impact as well. Finally, prolonged LOS was associated with a higher ratio of patients per medical doctor and per nurse. In contrast, a higher number of residents in the ED were associated with longer patient LOS.Originality/value: Previous studies on patient LOS have focused on patient-level factors, with little research on service-related factors. This study has addressed that gap by examining the association between LOS and multi-level factors including patient-, service- and organization-level factors. Patient-level factors included demographics, acuity, arrival shift, arrival mode and discharge type. Service-level factors consisted of first ward, ward transfer and complexity of care provided. Organizational factors consisted of three ratios: patients per MD, patients per nurse and patients per resident. The results add to the current understanding of factors that increase patient LOS in EDs and contribute to the body of knowledge on ED performance, operation management and quality of care. The study also provides practical and managerial insights that could be used to improve patient flow in EDs and reduce LOS.

Like other global mass gatherings, religious pilgrimages, such as Hajj, Arba’een, and the Hindu festival Kumbh Mela, attract millions of pilgrims to gather at specific holy sites on specific dates. During disease pandemics, mass gatherings can become super spreader events, causing exponential growth of infections in multiple regions. Epidemic modeling approaches can be valuable tools for studying the impact of mass gatherings on global health during disease outbreaks. To assess the use of epidemic models at religious pilgrimages, we compile published studies that proposed epidemic models at mass religious gatherings. A review of existing epidemic models at various religious gatherings highlights the role of epidemic modeling approaches in assessing the implications of religious pilgrimages on disease pandemics. All the articles surveyed showed a link between hosting religious gatherings and an increase in the number of cases of the simulated epidemic. In addition, we found that the SEIR mathematical model was the most common type developed with variations in some of the retrieved papers. The results reported in these studies motivate further investigation of the role of epidemic modeling and simulation in estimating the size and geographic scale of infections while hosting religious gatherings. Finally, we believe that this survey article draws attention to the application of epidemic models in the advanced planning of recurrent religious pilgrimages, as it is not feasible to cancel, suspend, or reallocate these pilgrimages. These epidemic models can provide a baseline for policymakers to determine which control measures should be implemented and when.

The religious pilgrimage of Hajj is one of the largest annual gatherings in the world. Every year approximately three million pilgrims travel from all over the world to perform Hajj in Mecca in Saudi Arabia. The high population density of pilgrims in confined settings throughout the Hajj rituals can facilitate infectious disease transmission among the pilgrims and their contacts. Infected pilgrims may enter Mecca without being detected and potentially transmit the disease to other pilgrims. Upon returning home, infected international pilgrims may introduce the disease into their home countries, causing a further spread of the disease. Computational modeling and simulation of social mixing and disease transmission between pilgrims can enhance the prevention of potential epidemics. Computational epidemic models can help public health authorities predict the risk of disease outbreaks and implement necessary intervention measures before or during the Hajj season. In this study, we proposed a conceptual agent-based simulation framework that integrates agent-based modeling to simulate disease transmission during the Hajj season from the arrival of the international pilgrims to their departure. The epidemic forecasting system provides a simulation of the phases and rituals of Hajj following their actual sequence to capture and assess the impact of each stage in the Hajj on the disease dynamics. The proposed framework can also be used to evaluate the effectiveness of the different public health interventions that can be implemented during the Hajj, including size restriction and screening at entry points.

Introduction: Self-efficacy is a critical determinant of students' academic success and overall life outcomes. Despite its recognized importance, research on predictors of self-efficacy using machine learning models remains limited, particularly within Muslim societies. This study addresses this gap by leveraging advanced machine learning techniques to analyze key factors influencing students' self-efficacy.Methods: An empirical dataset collected was used to examine self-efficacy among secondary school students in Muslim societies. Four machine learning algorithms-Decision Tree, Random Forest, XGBoost, and Neural Network-were employed to predict self-efficacy using two demographic variables and 10 socio-emotional, cognitive, and regulatory factors. The predictors included culturally relevant variables such as religious/spiritual beliefs and collectivist-individualist orientation. Model performance was assessed using root mean square error (RMSE) and r-squared (R²) metrics to ensure reliability and validity.Results: The results showed that Random Forest outperformed the other models in accuracy, as measured by R² and RMSE metrics. Among the predictors, self-regulation, problem-solving, and a sense of belonging emerged as the most significant factors, contributing to more than half of the model's predictive power. Other variables such as gratitude, forgiveness, empathy, and meaning-making displayed moderate predictive value, while gender, emotion regulation, and collectivist-individualist orientation had minimal impact. Notably, religious/spiritual beliefs and regional factors showed negligible influence on self-efficacy predictions.Discussion: This study enhances the understanding of factors influencing self-efficacy among students in Muslim societies and offers a data-driven foundation for developing targeted educational interventions. The findings highlight the utility of machine learning in education research, demonstrating its ability to uncover insights for equitable and effective decision-making. By emphasizing the importance of regulatory and socio-emotional factors, this research provides actionable insights to elevate student performance and well-being in diverse cultural contexts.