Rohit Das
Independent Researcher
West Bengal, India
Abstract
Predicting vaccine efficacy has long posed challenges due to the complex interplay of host immunity, pathogen variability, and population-specific factors. This manuscript presents the foundations for developing Artificial Intelligence (AI)-based predictive models to estimate vaccine efficacy by integrating immunological, epidemiological, and demographic data. Early AI systems, such as neural networks and decision trees, offer robust capabilities to analyze multidimensional datasets, identify latent patterns, and model non-linear relationships. This paper outlines the conceptual architecture of such predictive systems, examines prior immunoinformatics studies, and proposes a methodological pipeline grounded in data preprocessing, feature selection, model training, and validation. Emphasis is placed on balancing accuracy and interpretability using hybrid approaches like rule-based classifiers augmented with statistical learning. Preliminary evidence from published studies suggests that early-stage machine learning tools can significantly improve predictive insight into vaccine response heterogeneity across age, genetic markers, and comorbidities. The work highlights the need for interdisciplinary collaboration and high-quality longitudinal data to refine these models and ensure their translational utility in public health planning and personalized immunization strategies.
Keywords
Vaccine efficacy, predictive models, artificial intelligence, machine learning, immunoinformatics, public health
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