Epidemiological Covid-19 Outbreak Prediction and Analysis using Machine Learning

Manit Kakkar; Elisha Agarwal; Shaveta Arora

doi:https://doi.org/10.51976/ijari.822001

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Epidemiological Covid-19 Outbreak Prediction and Analysis using Machine Learning

Manit Kakkar, Elisha Agarwal, Shaveta Arora

Vol 8 , Issue 2 , April - June 2020 | Pages: 1-10 | Research Paper

https://doi.org/10.51976/ijari.822001

Received: February 15, 2020 | Revised: May 29, 2020 | Accepted: June 12, 2020 | Published Online: June 15, 2020

Author Details ( * ) denotes Corresponding author

1. Manit Kakkar, Department of Computer Science, The NorthCap University, Gurugram, Haryana, India

2. Elisha Agarwal, Department of Computer Science, The NorthCap University, Gurugram, Haryana, India

3. * Shaveta Arora, Department of Computer Science, The NorthCap University, Gurugram, Haryana, India (shavetaarora@ncuindia.edu)

In December 2019, coronavirus disease (Covid-19) showed in the seafood marketplace in Wuhan, China for which no treatment has been found till date. Since this disease is emerging widely in almost all parts of the world and no medication is found so far therefore research is needed to illuminate the study of coronaviruses. In this work, we have proposed various machine learning methods to analyse the pandemic coronavirus recovery rate and death rate in the real world. For this, polynomial regression, Decision tree regressor, Random Forest regressor and Long-short term memory (LSTM) model are utilized. The parameters are estimated, and predictions are made based on real-time dataset. Our proposed models tend to achieve high accuracy in prediction which will help to improve in-depth investigation

Keywords

Severe acute respiratory syndrome; Coronaviruses analysis; Machine learning; Regression

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