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Epidemo A Machine Learning Regression-Based

Authors

  • Joel Lee George

    Amal Jyothi College of Engineering
    Author
  • Karthik S Kumar

    Amal Jyothi College of Engineering
    Author
  • Riya Merce Thomas

    Amal Jyothi College of Engineering
    Author
  • Roshan Roy Varghese

    Amal Jyothi College of Engineering
    Author
  • Simy Mary Kurian

    Amal Jyothi College of Engineering
    Author

Abstract

In recent years, the world has witnessed the devastating consequences of disease outbreaks, highlighting the urgent need for effective epidemic management. An epidemic signifies the rapid transmission of illness to a substantial portion of a population within a short timeframe. The proposed system offers a proactive approach to this challenge by leveraging advanced Machine Learning (ML) regression tools. By analyzing diverse data sources such as historical disease trends, environmental conditions, and human behaviors, the system predicts the onset and spread of diseases, providing crucial early warnings for public health authorities and communities. Through timely implementation of preventive measures informed by these forecasts, authorities can mitigate the impact of epidemics, safeguard public health, and alleviate strain on healthcare systems. This proactive strategy underscores the importance of early intervention and data-driven approaches in combating and controlling disease outbreaks.

Keywords:

Epidemic Management
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Published

06-08-2025

Issue

Section

Articles

How to Cite

[1]
J. Lee George, K. S Kumar, R. Merce Thomas, R. Roy Varghese, and S. Mary Kurian, “Epidemo A Machine Learning Regression-Based ”, IJERA, vol. 4, no. 1, pp. 1–7, Aug. 2025, Accessed: Aug. 12, 2025. [Online]. Available: https://ijera.in/index.php/IJERA/article/view/176

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