Privacy-Enhancing Digital Contact Tracing with Machine Learning for Pandemic Response: A Comprehensive Review

Hang Ching-Nam; Tsai Yi-Zhen; Yu Pei-DuoChen JiasiTan Chee-Wei

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Privacy-Enhancing Digital Contact Tracing with Machine Learning for Pandemic Response: A Comprehensive Review

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The rapid global spread of the coronavirus disease (COVID-19) has severely impacted daily life worldwide. As potential solutions, various digital contact tracing (DCT) strategies have emerged to mitigate the virus's spread while maintaining economic and social activities. The computational epidemiology problems of DCT often involve parameter optimization through learning processes, making it crucial to understand how to apply machine learning techniques for effective DCT optimization. While numerous research studies on DCT have emerged recently, most existing reviews primarily focus on DCT application design and implementation. This paper offers a comprehensive overview of privacy-preserving machine learning-based DCT in preparation for future pandemics. We propose a new taxonomy to classify existing DCT strategies into forward, backward, and proactive contact tracing. We then categorize several DCT apps developed during the COVID-19 pandemic based on their tracing strategies. Furthermore, we derive three research questions related to computational epidemiology for DCT and provide a detailed description of machine learning techniques to address these problems. We discuss the challenges of learning-based DCT and suggest potential solutions. Additionally, we include a case study demonstrating the review's insights into the pandemic response. Finally, we summarize the study's limitations and highlight promising future research directions in DCT.

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《Big Data and Cognitive Computing》 |2023年第2期|共36页
作者
Hang Ching-Nam; Tsai Yi-Zhen; Yu Pei-DuoChen JiasiTan Chee-Wei;
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作者单位

Nanyang Technological University School of Computer Science and Engineering;

University of California Riverside Department of Computer Science and Engineering;

Chung Yuan Christian Univ;

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原文格式 PDF
正文语种英语
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digital contact tracing; COVID-19; computational epidemiology; machine learning; NETWORK; CHALLENGES; OUTBREAKS; EPIDEMIC; MODELS;

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Privacy-Enhancing Digital Contact Tracing with Machine Learning for Pandemic Response: A Comprehensive Review

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