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

Author:

Hang Ching-Nam1ORCID,Tsai Yi-Zhen2ORCID,Yu Pei-Duo3ORCID,Chen Jiasi2,Tan Chee-Wei4ORCID

Affiliation:

1. Department of Computer Science, City University of Hong Kong, Hong Kong

2. Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA

3. Department of Applied Mathematics, Chung Yuan Christian University, Taoyuan City 320314, Taiwan

4. School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore

Abstract

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.

Funder

Ministry of Science and Technology of Taiwan

Ministry of Education, Singapore, under its Academic Research Fund

NTU World Health Organization Collaborating Centre for Digital Health and Health Education

Hong Kong Innovation and Technology Fund

Publisher

MDPI AG

Subject

Artificial Intelligence,Computer Science Applications,Information Systems,Management Information Systems

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