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Abbas Ali

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Open access Jul 2026

Measles related pneumonia in unimmunized children: a deadly combination

Background: Despite the availability of an effective vaccine, measles is a significant cause of vaccine-preventable childhood morbidity and mortality in developing countries. The most common and life-threatening complication of measles is pneumonia and the vast majority of measles deaths are due to pneumonia, especially in poorly immunized children. Clarifying children's vaccination status may be useful to identify susceptible groups and increase preventive measures for measles related pneumonia. Objective: To determine frequency of unvaccinated children presenting with measles related pneumonia. Methodology: This study was a cross-sectional study carried out at Department of Pediatrics, Fauji Foundation Hospital, Rawalpindi from March 2026 to June 2026. Children aged 1–5 years with DMP were enrolled by non-probability consecutive sampling technique resulting in 255 children. The following data were collected on a structured proforma: demographic data, nutritional status, place of residence, parental education, socioeconomic status, and vaccination history. All data were analyzed on SPSS version 22.0. Data for the categorical variables were summarized as frequencies and percentages, and for the quantitative variables as means and standard deviations. The Chi-square test was used to assess the associations and a p value of ≤0.05 was considered statistically significant. Results: The mean age of 255 children with measles-related pneumonia was 3.2±1.1 years. There were 146 (57.3%) males and 109 (42.7%) females. Among the children, 153 (60.0%) were at least one dose of vaccine and 102 (40.0%) were unvaccinated against measles. Children in low socioeconomic families, malnourished children, children from rural areas and children whose mothers had lower education levels were significantly more likely to be unvaccinated (p<0.05). Conclusions: The proportion of children with pneumonia who presented with measles was high and many were not vaccinated. Improving the quality of routine immunization services and vaccine coverage, especially in hard-to-reach groups, must be achieved to stop avoidable childhood deaths and to lower severe measles complications.

Anwar Ullah, Syed Zulfiqar Haider, Faiza Jamil et al. · 0 citations
Review Open access 2026

Explainable machine learning for predicting cost and schedule overruns in irrigation projects: A safety-driven approach

Irrigation projects are essential for ensuring water security and sustainable agricultural development; however, they are highly susceptible to human, managerial, equipment, and environmental risks that contribute to cost overruns and schedule delays. Although machine learning (ML) has been widely applied to project performance prediction, its integration with validated safety indicators and explainable artificial intelligence for irrigation projects remains largely unexplored. Therefore, this study proposes an explainable machine learning framework for predicting cost overruns and schedule delays using safety indicators. A comprehensive dataset comprising 50 irrigation projects implemented in Iraq between 2022 and 2025 was developed. Twenty-three safety indicators were identified through a systematic literature review, refined through expert consultation, and validated using Partial Least Squares Structural Equation Modeling (PLS-SEM). Four ML algorithms, namely Decision Tree (DT), Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost), were developed and evaluated using 5-fold cross-validation. Model performance was assessed using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). The cross-validation results demonstrated that Random Forest achieved the most robust predictive performance for both cost overruns (R² = 0.912, RMSE = 1.685) and schedule delays (R² = 0.894, RMSE = 3.236). Furthermore, SHAP analysis identified human-related factors as the primary drivers of cost overruns and managerial factors as the dominant contributors to schedule delays. The proposed framework provides an interpretable decision-support tool for proactive safety management, early risk identification, and improved planning of irrigation projects.

Abbas Ali, Hatim Abd Al-Karim · 0 citations