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

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@article{IJASEIT14644,
   author = {Linta Islam and Mafizur Rahman and Nabila Ahmad and Tasnia Sharmin and Jannatul Ferdous Sorna},
   title = {A Crowdsourced Contact Tracing Model to Detect COVID-19 Patients using Smartphones},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {5},
   year = {2021},
   pages = {1818--1824},
   keywords = {Contact tracing; crowdsourcing; Covid-19; location tracking; mobile application.},
   abstract = {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.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14644},
   doi = {10.18517/ijaseit.11.5.14644}
}

EndNote

%A Islam, Linta
%A Rahman, Mafizur
%A Ahmad, Nabila
%A Sharmin, Tasnia
%A Sorna, Jannatul Ferdous
%D 2021
%T A Crowdsourced Contact Tracing Model to Detect COVID-19 Patients using Smartphones
%B 2021
%9 Contact tracing; crowdsourcing; Covid-19; location tracking; mobile application.
%! A Crowdsourced Contact Tracing Model to Detect COVID-19 Patients using Smartphones
%K Contact tracing; crowdsourcing; Covid-19; location tracking; mobile application.
%X 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.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14644
%R doi:10.18517/ijaseit.11.5.14644
%J International Journal on Advanced Science, Engineering and Information Technology
%V 11
%N 5
%@ 2088-5334

IEEE

Linta Islam,Mafizur Rahman,Nabila Ahmad,Tasnia Sharmin and Jannatul Ferdous Sorna,"A Crowdsourced Contact Tracing Model to Detect COVID-19 Patients using Smartphones," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 5, pp. 1818-1824, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.5.14644.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Islam, Linta
AU  - Rahman, Mafizur
AU  - Ahmad, Nabila
AU  - Sharmin, Tasnia
AU  - Sorna, Jannatul Ferdous
PY  - 2021
TI  - A Crowdsourced Contact Tracing Model to Detect COVID-19 Patients using Smartphones
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 5
Y2  - 2021
SP  - 1818
EP  - 1824
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Contact tracing; crowdsourcing; Covid-19; location tracking; mobile application.
N2  - 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.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14644
DO  - 10.18517/ijaseit.11.5.14644

RefWorks

RT Journal Article
ID 14644
A1 Islam, Linta
A1 Rahman, Mafizur
A1 Ahmad, Nabila
A1 Sharmin, Tasnia
A1 Sorna, Jannatul Ferdous
T1 A Crowdsourced Contact Tracing Model to Detect COVID-19 Patients using Smartphones
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 5
YR 2021
SP 1818
OP 1824
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Contact tracing; crowdsourcing; Covid-19; location tracking; mobile application.
AB 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.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14644
DO  - 10.18517/ijaseit.11.5.14644