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Paper Title : Prediction of COVID-19 Cases with Time Series Analysis and Machine Learning

ISSN : 2395-1303
Year of Publication : 2021
10.29126/23951303/IJET-V7I3P38

Authors: Sirisha Alamanda, Suresh Pabboju, Rishitha Reddy, Sheetal Naini

         


Abstract
- People in Millions have been infected and lakhs of people have lost their lives due to the Coronavirus. It is of utmost importance to predict the future infected cases and the rate of virus spread for advance preparation in the healthcare domain to avoid deaths. For the research community forecasting the spread of COVID-19 accurately, is an analytical and challenging problem in real-world. In this work time series analysis is done on the dataset taken from the ‘www.worldometers.info/coronavirus’ website. A polynomial regression technique and neural net model are used to do the statistical prediction of active cases and deaths for various countries, and the best models with least root mean square error is selected for the prediction. Being able to accurately predict the time of outbreak would significantly minimize the impact of the virus and help the government to be prepared to control the spread of virus

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Keywords
——Time Series Analysis, Polynomial Regression, Machine Learning, Time Series Forecasting, Pandemic, COVID-19