Real-Time Mapping of Epidemic Spread and Public  Alert System Using Machine Learning

Author(s)	Dr. Manjula H Nebagiri, Mr. Rakesh Hiremath, Mr. Sagar S, Ms. Soubhagya Suresh, Ms. Umme Kulsum
Country	India
Abstract	The paper presents a prototype web application for real-time epidemic monitoring, visualization, and alerting. The system integrates a CSV-backed case dataset, an optional machine-learning symptom classifier, server-side simulation of case updates, interactive geospatial visualization using Folium, and SMTP-based email alerts triggered by configurable thresholds. Real-time updates are broadcast to connected clients using Flask-SocketIO. This prototype is implemented in Python (Flask) and demonstrates a lightweight, deployable architecture suitable for rapid public-health dashboards, community reporting, and early-warning alerts. Results from the prototype show how streaming updates, map visualizations, and automated email notifications can be combined to provide situational awareness for regional epidemic spread. We discuss design choices, limitations, and directions for future work including robust data ingestion, multi-process deployment, and model-driven predictions.
Keywords	Epidemic monitoring, real-time visualization, Flask-SocketIO, Folium, SMTP alerts, machine learning, public- health dashboard, geospatial mapping
Field	Sociology > Health
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-01-11
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.65686

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International Journal For Multidisciplinary Research