A Crowdsourced Contact Tracing Model to Detect COVID-19 Patients using Smartphones

Linta Islam, Mafizur Rahman, Nabila Ahmad, Tasnia Sharmin, Jannatul Ferdous Sorna


Millions of people have died all across the world because of the COVID-19 outbreak. Researchers worldwide are working together and facing many challenges to bring out the proper vaccines to prevent this infectious virus. Therefore, in this study, a system has been designed which will be adequate to stop the outbreak of COVID-19 by spreading awareness of the COVID-19 infected patient situated area. The model has been formulated for Location base COVID-19 patient identification using mobile crowdsourcing. In this system, the government will update the information about inflected COVID-19 patients. It will notify other users in the vulnerable area to stay at 6 feet or 1.8-meter distance to remain safe. We utilized the Haversine formula and circle formula to generate the unsafe area. Ten thousand valid information has been collected to support the results of this research. The algorithm is tested for 10 test cases every time, and the datasets are increased by 1000. The run time of that algorithm is growing linearly. Thus, we can say that the proposed algorithm can run in polynomial time. The algorithm's correctness is also being tested where it is found that the proposed algorithm is correct and efficient. We also implement the system, and the application is evaluated by taking feedback from users. Thus, people can use our system to keep themselves in a safe area and decrease COVID patients' rate.


Contact tracing; crowdsourcing; Covid-19; location tracking; mobile application.

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DOI: http://dx.doi.org/10.18517/ijaseit.11.5.14644


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