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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Aman Khakharia & Vruddhi Shah & Sankalp Jain & Jash Shah & Amanshu Tiwari & Prathamesh Daphal & Mahesh Warang & Ninad Mehendale, 2021. "Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning," Annals of Data Science, Springer, vol. 8(1), pages 1-19, March.
    • Handle: RePEc:spr:aodasc:v:8:y:2021:i:1:d:10.1007_s40745-020-00314-9
    DOI: 10.1007/s40745-020-00314-9
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    References listed on IDEAS

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    1. Fotios Petropoulos & Spyros Makridakis, 2020. "Forecasting the novel coronavirus COVID-19," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-8, March.
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    Cited by:

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    2. Petar Radanliev & David Roure & Rob Walton & Max Kleek & Omar Santos & La’Treall Maddox, 2022. "What Country, University, or Research Institute, Performed the Best on Covid-19 During the First Wave of the Pandemic?," Annals of Data Science, Springer, vol. 9(5), pages 1049-1067, October.
    3. Hanem Mohamed & Salwa A. Mousa & Amina E. Abo-Hussien & Magda M. Ismail, 2022. "Estimation of the Daily Recovery Cases in Egypt for COVID-19 Using Power Odd Generalized Exponential Lomax Distribution," Annals of Data Science, Springer, vol. 9(1), pages 71-99, February.
    4. Lukman O. Oyelami & Matthew I. Ogbuagu & Olufemi M. Saibu, 2022. "Dynamic Interaction of COVID-19 Incidence and Stock Market Performance: Evidence from Nigeria," Annals of Data Science, Springer, vol. 9(5), pages 1009-1023, October.
    5. Ehab M. Almetwally, 2022. "The Odd Weibull Inverse Topp–Leone Distribution with Applications to COVID-19 Data," Annals of Data Science, Springer, vol. 9(1), pages 121-140, February.
    6. Muhammad Ahsan-ul-Haq & Mukhtar Ahmed & Javeria Zafar & Pedro Luiz Ramos, 2022. "Modeling of COVID-19 Cases in Pakistan Using Lifetime Probability Distributions," Annals of Data Science, Springer, vol. 9(1), pages 141-152, February.
    7. Elphas Okango & Henry Mwambi, 2022. "Dictionary Based Global Twitter Sentiment Analysis of Coronavirus (COVID-19) Effects and Response," Annals of Data Science, Springer, vol. 9(1), pages 175-186, February.

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