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

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.