Development of an AI-Based Disease Spread Forecasting Platform for Early Community Risk Mitigation

This paper presents an AI-based platform designed for early forecasting of infectious disease outbreaks using techniques adapted from time-series signal analysis. The system ingests and synthesizes multi-source public health data—including reported case counts, mobility patterns, environmental signals, and syndromic trends—to generate predictive risk scores for specific regions. Drawing from signal processing methodologies such as moving averages, volatility bands, and threshold-based triggers, the platform enables timely detection of emerging outbreak patterns. A dynamic rules engine and continuous feedback loop enhance forecast precision over time. Designed to support public health agencies and emergency planners, the system delivers real-time alerts through an interactive dashboard, promoting rapid response and resource allocation. This work contributes to the advancement of intelligent health surveillance systems, enabling scalable, explainable, and actionable outbreak forecasting in both crisis and endemic contexts.