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Predicting Transmission Rate of Coronavirus (COVID-19) Pandemic Using Machine Learning Techniques

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Computational Intelligence for COVID-19 and Future Pandemics

Abstract

COVID-19 is a single-stranded enclosed virus that was first discovered in Wuhan in December 2019. The 2019 novel coronavirus also known as COVID-19 has quickly transmitted to virtually all the nations of the world. This novel virus has posed a serious health challenge, and many countries are finding it difficult to preventing the spread of the deadly virus. In spite of all the money, time and energy invested in research toward finding a lasting solution to this problem, no remedy or vaccine has been recognized to effectively cure this disease. Under such circumstances, comprehending the rate of transmission of this virus is very crucial to curb its promulgation. Conventional techniques to decide the circulation rate of this infection are time wasting, and there is acute shortage of such technology in some places as well. Therefore, the need for a really quick and efficient machine learning (ML) model to tackle this menace has defied all known solutions. This paper used four high-performing ML methods to predict the rate of spread of COVID-19. The performance of these ML models was evaluated using the data obtained from patients that have been infected with COVID-19 illness between January 22, 2020 and August 7, 2020. Results of our experiments indicate that logistic regression (LR) outperforms every other model considered in this work. The implication of this is that LR model is a promising classifier that can be used to effectively predict the rate of transmission of COVID-19. We also discovered from our results that there is growing tendency of COVID-19 transmission in every other continent of the world with the exception of some parts of Africa and Oceania where COVID-19 transmission has been decelerated and curve flattened.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Federal University Kashere, Gombe, Nigeria

    David Opeoluwa Oyewola & Asabe Ibrahim

  2. Department of Mathematical Sciences, University of Maiduguri, Maiduguri, Nigeria

    Emmanuel Gbenga Dada

  3. Department of Mathematics, Baze University, Abuja, FCT, Nigeria

    K. A. Al-Mustapha

  4. Department of Computer Science, Federal College of Education, Zaria, Nigeria

    Ayotunde Alaba Fadele

  5. Department of Computer Engineering, University of Maiduguri, Maiduguri, Nigeria

    Stephen Bassi Joseph

Authors
  1. David Opeoluwa Oyewola
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  2. Emmanuel Gbenga Dada
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  3. K. A. Al-Mustapha
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  4. Ayotunde Alaba Fadele
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  5. Stephen Bassi Joseph
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  6. Asabe Ibrahim
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Corresponding author

Correspondence to Emmanuel Gbenga Dada .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Suleyman Demirel University, Isparta, Turkey

    Utku Kose

  2. Graduate School of Information, Waseda Univeristy, Shinjuku, Japan

    Junzo Watada

  3. Dept. of Computer Technology, Afyon Kocatepe University, Afyonkarahisar, Turkey

    Omer Deperlioglu

  4. Facultad de Ingeniería, Universidad Panamericana, Mexico City, Mexico

    Jose Antonio Marmolejo Saucedo

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Oyewola, D.O., Dada, E.G., Al-Mustapha, K.A., Fadele, A.A., Joseph, S.B., Ibrahim, A. (2022). Predicting Transmission Rate of Coronavirus (COVID-19) Pandemic Using Machine Learning Techniques. In: Kose, U., Watada, J., Deperlioglu, O., Marmolejo Saucedo, J.A. (eds) Computational Intelligence for COVID-19 and Future Pandemics. Disruptive Technologies and Digital Transformations for Society 5.0. Springer, Singapore. https://doi.org/10.1007/978-981-16-3783-4_3

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