AI-Based Predictive Analytics for Epidemic Forecasting and Healthcare Resource Optimization

Abstract

This study presents an AI-based predictive analytics framework designed to improve epidemic forecasting accuracy and optimize healthcare resource utilization. The analysis of forecasting performance demonstrates that AI-driven models closely align with actual epidemic case trends over time, maintaining prediction errors within a narrow margin of approximately 2–5%. This high level of accuracy enables timely interventions and supports proactive decision-making. Compared to traditional models, AI-based approaches effectively capture nonlinear patterns and dynamic variations in epidemic data, resulting in more reliable predictions. In addition to forecasting accuracy, the study evaluates the efficiency of healthcare resource allocation. The results show that AI-driven models significantly improve the utilization of critical resources, including hospital beds, ICU units, ventilators, and medical staff. Resource efficiency increases by approximately 20–25% compared to baseline systems, highlighting the effectiveness of predictive analytics in optimizing resource distribution during peak demand periods. Furthermore, the comparison of prediction error rates across different models reveals that hybrid AI approaches outperform traditional statistical methods such as ARIMA and deep learning models like LSTM and Transformer networks. The hybrid model achieves the lowest error rate, demonstrating its ability to capture both short-term fluctuations and long-term trends. Overall, the findings indicate that AI-based predictive analytics provides a robust and scalable solution for epidemic forecasting and healthcare resource optimization. By combining high prediction accuracy with efficient resource management, the proposed framework enhances healthcare system resilience and supports effective response to public health crises.