Prediction of COVID-19 epidemic curve of India using supervised learning approach. (15th January 2022)

• Record Type: Journal Article
• Authors: Mongia, Shweta; Sharma, Sugandha; Natarajan, Jaisankar; Kumar, Manoj; Arora, Vasudha; Stephan, Thompson; Shankar, Achyut; Gupta, Pragya; Kachhawaha, Raghav
• Is Part Of: International journal of computer applications technology. Volume 66:Number 3/4(2021)
• Page Start: 433
• Page End: 441
• Publication Date: 2022-01-15
• Subjects: supervised learning -- polynomial regression model -- COVID-19 -- prediction -- epidemic curve; Technology -- Data processing -- Periodicals; 620.00285
• Languages: English
• ISSNs: 0952-8091

Abstract

The recent outbreak of the COVID-19 pandemic, a neo zoonotic infectious disease, has caused high mortality worldwide. The need of the hour is to equip the governments with early detection, prevention, and mitigation of such contagious diseases. In this paper, a supervised learning approach of the polynomial regression model is used for the prediction of COVID-19 cases in terms of the number of confirmed cases (CC), death cases (DC), and recovered cases (RC) in India. Authors have also predicted death rates and recovery rates. The recovery rate on 25th April 2020 is 21.97% and by 1st June 2020 this rate will increase to 79%. In addition to this, authors have projected a monthly percentage increase in the number of CC from 1st May 2020 to 1st December 2020. This analysis would help and enable the concerned authorities in bringing effective preventive measures into action in the process of decision making.