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@article{187383,
        author = {Ravindranath A and Rohit Rajagopal and Shubhankar Vishwakarma and Uday Sankar and Dr. A Prakash},
        title = {OutbreakAtlas: Advanced Epidemic Alert System for Instant Epidemic Monitoring and Hotspot Identification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {4382-4388},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187383},
        abstract = {For an effective resolution of an infectious disease outbreak, timely detection and intervention are essential. OutbreakAtlas serves as a proactive alert system for epidemics.  Using a density-based spatial clustering technique, it will almost instantly identify disease hotspots and produce map visualizations of them.    The density-based spatial clustering (DBSCAN) algorithm will be used by the system to gather geotagged reported incidence cases from online sources.    This algorithm quickly displays filtered data on an interactive map by classifying and identifying geo-tagged reported cases.    Any reports pertaining to a high-risk hotspot mentioned in the original report are highlighted. An important improvement over traditional epidemiological surveillance systems is the integration of machine learning spatial clustering algorithm, crowdsourced case reporting, and interactive mapping. Additionally, the detection and tracking of potential epidemic threats will be accelerated by combining spatial clustering techniques with near real-time machine learning across large regions and dense epidemiological data. This will improve centralized reporting of emerging risks at the local and regional levels.},
        keywords = {Epidemic Surveillance, DBSCAN, Clustering, NLP,Public Health Informatics, Real-Time Visualization, Outbreak Detection.},
        month = {November},
        }