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

Krishna Mohan Surapaneni; Manmohan Singhal; Ashish Joshi

doi:10.55489/njcm.160920255736

Authors

Krishna Mohan Surapaneni School of Pharmaceutical & Population Health Informatics, DIT University, Dehradun, Uttarakhand, India; Panimalar Medical College Hospital & Research Institute, Chennai, Tamil Nadu, India
Manmohan Singhal School of Pharmaceutical & Population Health Informatics, DIT University, Dehradun, Uttarakhand, India
Ashish Joshi School of Public Health, The University of Memphis, Memphis, TN 38152, USA

DOI:

https://doi.org/10.55489/njcm.160920255736

Keywords:

Long COVID, Post-Acute Sequelae of SARS-CoV-2, Clinical Decision Support System, Risk Stratification, Digital Health, Symptom Monitoring, Post-COVID Management

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 confirmed 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 individualized 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, unvaccinated participants, and those hospitalized or admitted to ICU during acute infection. The logic model enabled 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.

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