Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning

Aman Khakharia, Vruddhi Shah, Sankalp Jain, Jash Shah, Amanshu Tiwari, Prathamesh Daphal, Mahesh Warang and Ninad Mehendale ()
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Aman Khakharia: K. J. Somaiya College of Engineering
Vruddhi Shah: K. J. Somaiya College of Engineering
Sankalp Jain: K. J. Somaiya College of Engineering
Jash Shah: K. J. Somaiya College of Engineering
Amanshu Tiwari: K. J. Somaiya College of Engineering
Prathamesh Daphal: K. J. Somaiya College of Engineering
Mahesh Warang: K. J. Somaiya College of Engineering
Ninad Mehendale: K. J. Somaiya College of Engineering

Annals of Data Science, 2021, vol. 8, issue 1, No 1, 19 pages

Abstract: Abstract The Coronavirus Disease-2019 (COVID-19) pandemic persists to have a mortifying impact on the health and well-being of the global population. A continued rise in the number of patients testing positive for COVID-19 has created a lot of stress on governing bodies across the globe and they are finding it difficult to tackle the situation. We have developed an outbreak prediction system for COVID-19 for the top 10 highly and densely populated countries. The proposed prediction models forecast the count of new cases likely to arise for successive 5 days using 9 different machine learning algorithms. A set of models for predicting the rise in new cases, having an average accuracy of 87.9% ± 3.9% was developed for 10 high population and high density countries. The highest accuracy of 99.93% was achieved for Ethiopia using Auto-Regressive Moving Average (ARMA) averaged over the next 5 days. The proposed prediction models used by us can help stakeholders to be prepared in advance for any sudden rise in outbreak to ensure optimal management of available resources.

Keywords: COVID-19 outbreak prediction; COVID-19; Machine learning (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s40745-020-00314-9

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