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The novel coronavirus outbreak was first reported in late December 2019 and more than 7 million people were infected with this disease and over 0.40 million worldwide lost their lives. The first case was diagnosed on 30 January 2020 in India and the figure crossed 0.24 million as of 6 June 2020. This paper presents a detailed study of recently developed forecasting models and predicts the number of confirmed, recovered, and death cases in India caused by COVID-19. The correlation coefficients and multiple linear regression applied for prediction and autocorrelation and autoregression have been used to improve the accuracy. The predicted number of cases shows a good agreement with 0.9992 R-squared score to the actual values. The finding suggests that lockdown and social distancing are two important factors that can help to suppress the increasing spread rate of COVID-19.


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Analysis and Predictions of Spread, Recovery, and Death Caused by COVID-19 in India

Show Author's information Rajani KumariSandeep Kumar( )Ramesh Chandra PooniaVijander SinghLinesh RajaVaibhav BhatnagarPankaj Agarwal
Department of Information Technology and Computer Application, JECRC University, Jaipur, Rajasthan 303905, India
CHRIST (Deemed to be University),Bangalore, Karnataka 560029, India
Amity University Rajasthan, Jaipur, Rajasthan 303002, India
Manipal University Jaipur, Rajasthan 303007, India

Abstract

The novel coronavirus outbreak was first reported in late December 2019 and more than 7 million people were infected with this disease and over 0.40 million worldwide lost their lives. The first case was diagnosed on 30 January 2020 in India and the figure crossed 0.24 million as of 6 June 2020. This paper presents a detailed study of recently developed forecasting models and predicts the number of confirmed, recovered, and death cases in India caused by COVID-19. The correlation coefficients and multiple linear regression applied for prediction and autocorrelation and autoregression have been used to improve the accuracy. The predicted number of cases shows a good agreement with 0.9992 R-squared score to the actual values. The finding suggests that lockdown and social distancing are two important factors that can help to suppress the increasing spread rate of COVID-19.

Keywords:

COVID-19, regression, correlation, machine learning, prediction
Received: 07 May 2020 Revised: 14 June 2020 Accepted: 28 July 2020 Published: 01 February 2021 Issue date: June 2021
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Publication history

Received: 07 May 2020
Revised: 14 June 2020
Accepted: 28 July 2020
Published: 01 February 2021
Issue date: June 2021

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