AI-Driven Epidemic Response: Optimizing Disease Prediction and Resource Allocation

Abstract

The global spread of COVID-19 has exposed vulnerabilities in healthcare systems and highlighted the need for predictive tools to mitigate its impact. This study employs machine learning (ML) techniques, including Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XG-Boost), to predict disease spread and optimize resource allocation. Using datasets enriched with features like population density, healthcare capacity, and mobility patterns, XG-Boost achieved superior performance, attaining 100% accuracy and surpassing RF (99%) and SVM (76%). Advanced methods, such as SHAP (SHapley Additive Explanations), provided critical insights into key factors driving disease progression, enabling transparent and interpretable predictions. The findings underscore the transformative potential of AI-driven solutions in guiding ICU bed allocation, ventilator distribution, and healthcare resource deployment, particularly in resource-constrained settings. While this study demonstrates the scalability and precision of ML frameworks for epidemic management, it also acknowledges limitations, such as dataset imbalance, and suggests integrating real-time data for enhanced predictions. By advancing AI applications in public health, this research offers a scalable and practical framework to strengthen global preparedness and response to future health crises.