| Title: | Analysis of COVID-19 tracking tool in india: case study of aarogya setu mobile application : Case Study of Aarogya Setu Mobile Application |
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| Authors: | ID Gupta, Rajan (Author)
ID Bedi, Manan (Author)
ID Goyal, Prashi (Author)
ID Wadhera, Srishti (Author)
ID Verma, Vaishnavi (Author) |
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| Files: |  https://dl.acm.org/doi/10.1145/3416088
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| Language: | English |
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| Work type: | Unknown |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | UNG - University of Nova Gorica
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| Abstract: | COVID-19 tracking tools or contact-tracing apps are getting developed at a rapid pace by different governments in their respective countries. This study explores one such tool called Aarogya Setu, developed by the Government of India. It is a mobile application developed under the Health Ministry, as a part of the E-Governance initiative, to track and sensitize the citizens of India in a joint battle against COVID-19 spread. The study aims to understand various useful features of this tool and to present different concepts of data science applied within the application along with its importance in managing the ongoing pandemic. The App uses Bluetooth and GPS technologies to alert a user when they are nearby a COVID-19 infected person. The application uses various Data Science concepts such as Classification, Association Rule Mining, and Clustering to analyze COVID-19 spread in India. The study also shows potential upgradations in the application, which includes usage of Artificial Intelligence and Computer Vision to detect COVID-19 patients. The study would be useful for mobile technology professionals, data science professionals, medical practitioners, health-related frontline workers, public administrators, and government officials. |
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| Keywords: | COVID-19, India, contact-tracing, tracking tool, Bluetooth, GPS, COVID19 reporting tool, Aarogya Setu |
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| Year of publishing: | 2020 |
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| Number of pages: | str. 1-8 |
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| Numbering: | Vol. 1, no. 4 |
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| PID: | 20.500.12556/RUNG-6392  |
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| COBISS.SI-ID: | 57960707 |
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| UDC: | 004 |
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| ISSN on article: | 2639-0175 |
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| DOI: | 10.1145/3416088 |
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| NUK URN: | URN:SI:UNG:REP:L49OLAN9 |
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| Publication date in RUNG: | 01.04.2021 |
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| Views: | 1993 |
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| Downloads: | 60 |
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