sis 18: e19

Research Article

Clustering based Contact Tracing Analysis and Prediction of SARS-CoV-2 Infections

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  • @ARTICLE{10.4108/eai.3-11-2021.171756,
        author={Meenu Gupta and Rakesh Kumar and Sunil Kumar Chawla and Sunny Mishra and Sourabh Dhiman},
        title={Clustering based Contact Tracing Analysis and Prediction of SARS-CoV-2 Infections},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={11},
        keywords={Clustering algorithm, Contact tracing, DBSCAN, SARS-CoV-2, COVID-19},
        doi={10.4108/eai.3-11-2021.171756}
    }
    
  • Meenu Gupta
    Rakesh Kumar
    Sunil Kumar Chawla
    Sunny Mishra
    Sourabh Dhiman
    Year: 2021
    Clustering based Contact Tracing Analysis and Prediction of SARS-CoV-2 Infections
    SIS
    EAI
    DOI: 10.4108/eai.3-11-2021.171756
Meenu Gupta1, Rakesh Kumar1, Sunil Kumar Chawla1,*, Sunny Mishra1, Sourabh Dhiman1
  • 1: Computer Science and Engineering, University Institute of Engineering, Chandigarh University, Mohali, Punjab, India
*Contact email: drskchawla.1983@gmail.com

Abstract

INTRODUCTION: Contact tracing is a method to track the victims, which have been infected from the host with any particular disease. Therefore, clustering based machine learning techniques can be employed for contact tracing. Contact tracing can be automated by using technology and thus helps us in producing much more accurate and efficient results.

OBJECTIVES: This work aims at finding usefulness of clustering techniques for contact tracing. Two different clustering techniques namely density-based clustering and partitioning-based clustering have been used to analyse corresponding results for COVID-19 infected cases. The dataset is generated from a mock data generator with certain assumptions.

RESULTS: The paper compares DBSCAN and K-means for contact tracing for COVID-19 Pandemic. The comparative analysis of two algorithms is presented.

CONCLUSION: The effectiveness of certain clustering algorithms in COVID-19 contact tracing is analysed. DBSCAN performs well for clustering tasks. This work only focuses on possible techniques useful for contact tracing and does not claim any medical accuracy.