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

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 epidemiol...

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Bibliographic Details
Published in:Big data and cognitive computing 2023-06, Vol.7 (2), p.108
Main Authors: Hang, Ching-Nam, Tsai, Yi-Zhen, Yu, Pei-Duo, Chen, Jiasi, Tan, Chee-Wei
Format: Article
Language:English
Subjects:
Artificial intelligence
Case studies
computational epidemiology
Contact tracing
Coronaviruses
COVID-19
Datasets
Deep learning
digital contact tracing
Disease transmission
Epidemics
Epidemiology
Infections
Intervention
Machine learning
Medical research
Optimization
Pandemics
Pharmaceuticals
Privacy
Process parameters
Quarantine
Taxonomy
Viral diseases
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Summary: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.
ISSN:2504-2289
2504-2289
DOI:10.3390/bdcc7020108