Vol. 3 No. 2 (2023): Journal of Deep Learning in Genomic Data Analysis
Articles

Machine Learning Models for Early Detection and Monitoring of Infectious Diseases: AI Approaches for Enhancing Surveillance and Response Strategies

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  • Dr. Jean-Pierre Berger
Dr. Jean-Pierre Berger
Associate Professor of Artificial Intelligence, Université Claude Bernard Lyon 1, France
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Published 28-12-2023

Keywords

  • Early Detection,
  • Infectious Diseases
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Jean-Pierre Berger, “Machine Learning Models for Early Detection and Monitoring of Infectious Diseases: AI Approaches for Enhancing Surveillance and Response Strategies”, Journal of Deep Learning in Genomic Data Analysis, vol. 3, no. 2, pp. 59–71, Dec. 2023, Accessed: Dec. 03, 2024. [Online]. Available: https://thelifescience.org/index.php/jdlgda/article/view/50

Abstract

Infectious diseases have been at the forefront of major global pandemics, episodic regional outbreaks that cripple public health, and ongoing threats recurrently experienced at national and subnational levels. Emerging and reemerging pathogens have the potential to cause large-scale social, economic, and political disruptions. Highly interconnected societies, combined with unprecedented ease and speed of global travel, make the rapid surveillance, detection, and response to especially infectious diseases a real necessity. Surveillance systems are an important part of the public health response to infectious diseases and can be used to monitor changing trends, to identify possible outbreaks, to indicate areas where additional data collections are needed, and to evaluate the impact of control and prevention programs. Certain infectious diseases evolve rapidly and necessitate robust surveillance methods for early detection and monitoring activities to mitigate the impact of a disease.

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