AI-Enhanced Strategies for COVID-19 Vaccination and Booster Prioritization: A Comprehensive Framework - AlShaya, Sara; Gopakumar, Aji; Raghupathy, Rahul; Mukherjee, Badshah; Abou Elias, Bachir; Jamal Mohideen, Sheik; Mathur, Vibhor; Kurakula, Sudheer (National Journal of Community Medicine, 2024)
Background: Distributing vaccines efficiently during the COVID-19 pandemic presented significant logistical challenges. To address the need for identifying populations at risk of breakthrough infections and those requiring booster shots, Emirates Health Services (EHS) developed a framework utilizing AI-driven digital solutions. Objective: To develop a machine learning (ML) model to identify individuals at risk of breakthrough infections and in need of booster doses, aiming to prioritize booster administration and reduce repeated infections among fully vaccinated individuals in the Northern Emirates of the UAE. Methods: A monitoring dashboard was developed using the EHS Intelligence (PaCE) platform. The study, conducted in three phases, created models to predict infection risk, COVID-19 severity in ICU patients and breakthrough infection risk, using data from the Wareed EMR system. Results: The AI models accurately identified high-risk individuals and predicted ICU mortality, achieving AUCs of 75% and 74% for infection risk, 94% and 91% for ICU mortality in training and validation datasets, observed 79% AUC with 85% accuracy for identifying high-risk groups for booster vaccination. Conclusion: The integration of AI in vaccination prioritization demonstrated its potential to enhance public health initiatives and improve pandemic management in the UAE.