AI-Driven Epidemic Intelligence for Influenza Forecasting in India

Influenza remains a major public health challenge in India due to its seasonal outbreaks and rapid transmission dynamics. Accurate and timely forecasting of influenza cases is essential for effective epidemic preparedness and response. This study proposes an AI-driven epidemic intelligence framework for forecasting influenza trends in India using machine learning techniques. Historical influenza surveillance data are analyzed and preprocessed to capture temporal patterns and seasonal variations. Predictive models based on time-series learning are developed to estimate future influenza incidence. The proposed approach aims to support public health authorities by enabling early warning signals, improving resource allocation, and strengthening data-driven decision-making for epidemic control. The results demonstrate the potential of artificial intelligence in enhancing influenza forecasting accuracy and contributing to proactive public health surveillance in the Indian context.