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Journal of Communications and Networks
Article . 2021 . Peer-reviewed
License: CC BY NC
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Energy efficient contact tracing and social interaction based patient prediction system for COVID-19 pandemic

Authors: Moremada, C. (Charuka); Sandeepa, C. (Chamara); Dissanayaka, N. (Nadeeka); Gamage, T. (Tharindu); Liyanage, M. (Madhusanka);

Energy efficient contact tracing and social interaction based patient prediction system for COVID-19 pandemic

Abstract

Abstract Due to the spread of Coronavirus disease 2019 (COVID-19), the world has encountered an ongoing pandemic to date. It is a highly contagious disease. In addition to the vaccination, social distancing and isolation of patients are proven to be one of the commonly used strategies to reduce the spread of disease. For efficient social distancing, contact tracing is a critical requirement in the incubation period of 14-days of the disease to contain any further spread. However, we identify that there is a lack of reliable and practical social interaction tracking methods and prediction methods for the probability of getting the disease. This paper focuses on user tracking and predicting the infection probability based on these social interactions. We first developed an energy-efficient BLE (Bluetooth Low Energy) based social interaction tracking system to achieve this. Then, based on the collected data, we propose an algorithm to predict the possibility of getting the COVID-19. Finally, to show the practicality of our solution, we implemented a prototype with a mobile app and a web monitoring tool for healthcare authorities. In addition to that, to analyze the proposed algorithm’s behaviour, we performed a simulation of the system using a graph-based model.

Country
Finland
Subjects by Vocabulary

Microsoft Academic Graph classification: Computer science Machine learning computer.software_genre Pandemic medicine Isolation (database systems) business.industry Social distance Tracking system medicine.disease Social relation Contagious disease Graph (abstract data type) Artificial intelligence business computer Efficient energy use

Keywords

Internet of things, social interaction tracing, Computer Networks and Communications, infection prediction, energy efficiency, contact tracing algorithm, SARS-CoV-2, COVID-19, Bluetooth Low Energy, Information Systems

42 references, page 1 of 5

[1] “Naming the coronavirus disease (covid-19) and the virus that causes it.” [Online]. Available: https://www.who.int/emergencies/diseases/nov el-coronavirus-2019/technical-guidance/naming-the-coronavirus-diseas e-(covid-2019)-and-the-virus-that-causes-it

[2] S. Begley, Jim, Alan, and C. M, “New digital tools could speed up Covid-19 contact tracing,” Apr. 2020. [Online]. Available: https://www. statnews.com/2020/04/02/coronavirus-spreads-too-fast-for-contact-traci ng-digital-tools-could-help

[3] A. Greenberg, “How Apple and Google are enabling Covid-19 bluetooth contact-tracing.” [Online]. Available: https://www.wired.com/story/appl e-google-bluetooth-contact-tracing-covid-19/

[4] K. Foy and L. Laboratory, “Bluetooth signals from your smartphone could automate Covid-19 contact tracing while preserving privacy,” Apr 2020. [Online]. Available: http://news.mit.edu/2020/bluetooth-covid-19 -contact-tracing-0409

[5] “Help speed up contact tracing with tracetogether.” [Online]. Available: https://www.gov.sg/article/help-speed-up-contact-tracing-with-tracetog ether

[6] A. G. D. of Health, “COVIDSafe app,” May 2020. [Online]. Available: https://www.health.gov.au/resources/apps-and-tools/covidsafe-app

[7] “Modes of transmission of virus causing COVID-19: implications for IPC precaution recommendations.” [Online]. Available: https://www.wh o.int/news-room/commentaries/detail/modes-of-transmission-of-virus-c ausing-covid-19-implications-for-ipc-precaution-recommendations

[8] S. A. Lauer, et al., “The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: Estimation and application,” Annals Internal Medicine, 2020.

[9] K. Townsend, C. Cuf´ı, R. Davidson et al., Getting started with Bluetooth low energy: Tools and techniques for low-power networking. “O'Reilly Media, Inc.”, 2014.

[10] S. Raza, P. Misra, Z. He, and T. Voigt, “Building the Internet of things with bluetooth smart,” Ad Hoc Networks, vol. 57, pp. 19-31, 2017.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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