Decision-Making Logic Model for Risk Stratification Using iALERTS (Informatics Analytics for Long-Term Evaluation and Repercussions Tracking of SARS-Cov-2 Infection)

Abstract

Background: Long COVID presents a significant public health challenge with its wide-ranging and persistent symptoms. However, there remains a lack of structured tools to identify, stratify, and manage individuals at risk of Long COVID. This study aims to develop the decision-making logic model for risk stratification using iALERTS platform. Methods: This is a mixed-methods, quasi-experimental study. Data were collected from 684 adults with con-firmed COVID-19 who were at least 12 weeks post-recovery. A validated survey captured sociodemographic data, clinical history, anthropometry, vaccination status, and a comprehensive symptom profile. A rule-based decision-making logic model was embedded within iALERTS, incorporating ten key factors to generate indi-vidualized risk assessments. Results: Fatigue (80.8%), cough (83.3%), cognitive dysfunction (68.3%) and myalgia (74.3 %) were the most common persistent symptoms. High-risk groups included females, older adults, individuals with obesity, un-vaccinated participants, and those hospitalized or admitted to ICU during acute infection. The logic model en-abled automated risk stratification into low, moderate, or high categories, guiding clinical recommendations for monitoring, referrals, and rehabilitation. Conclusion: The iALERTS platform offers a novel informatics-driven solution for risk stratification and management of Long COVID. Its decision logic integrates validated clinical and demographic predictors with real-time symptom data.

Publication Title

National Journal of Community Medicine

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