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@article{172260,
        author = {Manohar C and Adithya N S and Sanath Kumar P S and Nydile M C and Dr. Selvaraj  Poornima},
        title = {Real Time Mapping of Epidemic Spread},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {2451-2454},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172260},
        abstract = {Infectious disease outbreaks represent a crucial contributor to morbidity and mortality around the globe. Real-time epidemic spread mapping creates the potential to predict not only geographic spread of disease but also case counts for improving public health intervention at outbreak events. The integrated healthcare platform in this study addresses the objective of enhancing epidemic response through mathematical modeling, machine learning, and user-centric functionalities in synergy. It enables the healthcare provider to manage appointments efficiently, input epidemic-related data in real-time, and get access to a dynamic dashboard with detailed analytics. Patients can easily register, get appointments scheduled, and track their recovery. The dashboard of the platform provides granular insights into epidemic trends by applying day and month-wise filters, downloadable patient records, and graphical representations of case and recovery statistics. Such a data-driven approach encourages
informed decision-making, empowers stakeholders to respond proactively to epidemics. It addresses key barriers such as fragmented data, technological adoption Challenges and security concerns notwithstanding, this platform is a transformative step toward a resilient and adaptive healthcare ecosystem.},
        keywords = {Integrated Healthcare Platform, Epidemic Management, Machine Learning, Mathematical Modeling, Real-Time Data, Pandemic Response, Data-Driven Decision Making.},
        month = {January},
        }