Sanchana.R ,Nithya Devi.S , Mercy.P ,Dhanush Kumar.S
COVID- 19 arose in the city of Wuhan in China. The situation started to be more critical when numerous number of person started to get infected which in turns increases the number of death cases. The novel coronavirus (COVID-19) that was first reported at the end of 2019 has impacted almost every aspect of life as we know it. This paper focuses on the incidence of the disease in India Using three simple machine learning algorithms—the linear regression model, polynomial regression and Support Vector machine, we model the daily and cumulative incidence of COVID-19 in the country during the early stage of the outbreak, and compute estimates for basic measures of the infectiousness of the disease including the active cases, death cases, growth rate, mortality rate and recovery rate. Estimates of the growth factor is calculated using the new confirmed, recovered and death cases divided using the previous confirmed, recovered and death cases. The growth factor above 1 indicates the increase in corresponding cases. The growth cases below 1 indicate the trending downward which indicates the sign of exponential growth. The predictive ability of the polynomial regression model was found to give a better fit and simple estimates of the daily incidence.
Journal Name :
EPRA International Journal of Research & Development (IJRD)

Published on : 2022-05-02

Vol : 7
Issue : 4
Month : April
Year : 2022
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