COMPUTATIONAL METHODS FOR REAL-TIME EPIDEMIC TRACKING AND PUBLIC HEALTH MANAGEMENT
Keywords:
Epidemic Tracking, Public Health Management, Computational Epidemiology, Machine Learning, Real-time Surveillance, Digital EpidemiologyAbstract
The rapid evolution of infectious diseases demands innovative approaches for real-time epidemic tracking and public health management. Computational methods, leveraging advancements in data analytics, machine learning, and digital epidemiology, offer transformative tools to detect, predict, and mitigate disease outbreaks. This paper examines various computational techniques, including agent-based models, network analysis, and machine learning algorithms, to assess their efficacy in tracking epidemics and aiding public health responses. Through a comprehensive literature review, we explore current applications, challenges, and future prospects in the field. Our analysis highlights the potential of computational approaches in enhancing the agility of public health responses, while also acknowledging the complexities of data integration and real-time analysis.
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Copyright (c) 2023 Dhruv Sanjay Jain (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
