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.
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