Utilization of Grid Neural Network Model and RT-PCR test to detect the COVID-19 Patients and to avoid the Spreading of SARS-CoV-2
Ajendra Kumar1, Preet Pal Singh2, Dipa Sharma3, Pawan Joshi4

1Ajendra Kumar, Department of Mathematics and Statistics, Gurukula Kangri Vishwavidyalaya Haridwar (U.K), India.
2Preet Pal Singh, Department of Mathematics, Pt. L.M.S. (P.G) College, Rishikesh (U.K), India.
3Dipa Sharma, Department of Mathematics, S.D.M. Government (P.G) College, Doiwala (U.K), India.
4Pawan Joshi*, Department of Mathematics and Statistics, Gurukula Kangri Vishwavidyalaya, Haridwar (U.K), India.

Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 44-50 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.C4253099320 | DOI: 10.35940/ijrte.C4253.099320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In December 2019, a new virus, also named a novel coronavirus, started as an emerging pathogen for humans and resulted in a pandemic. World Health Organization (WHO) called this novel coronavirus as COVID-19 on 11 February 2020, and the virus responsible for causing COVID-19 is SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), which is a positive-stranded RNA virus. This paper proposed an artificial neural network model in a grid computing system to identify COVID-19 patients. It can help us to identify the suspected patients and shortlist those patients who need to check by the RT-PCR test kit. The purpose of this research is to increase the time efficiency to test those patients, which has a higher chance of getting affected by COVID-19. Increasing the time efficiency in this type of pandemic situation can make a huge impact on reducing the fatality rate. This is because, according to ICMR, 1,191,946 samples have been tested as of 5 May, and 46,433 individuals have been confirmed positive. It means that only 3.85% of persons get positive results and 96.15% persons with a negative result. It implies that the time to test this 96.15% of cases is wasted. Hence we aim to detect the COVID-19 patients in less time and utilize this large amount of time to test those at higher risk of being affected by this epidemic (COVID-19). This model will also help those countries to overcome the problem of the shortage of this type of test kits such as – RT-PCR.
Keywords: Artificial Neural Network (ANN), Grid Computing, SARS-CoV-2, Reverse Transcription Polymerase Chain Reaction (RT-PCR) test.