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Understanding COVID-19: The Role of Computational Intelligence pp 225–244Cite as

Potential of Deep Learning Algorithms in Mitigating the Spread of COVID-19

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 963))

Abstract

COVID-19 pandemic has become endemic and has plunged the global community into a perilous situation pervaded with an economic recession, loss of jobs, and the death of thousands of people. It spreads exponentially around the world, affects 213 countries and territories as well as two international conveyances. Yet, the pandemic has neither clinically proven drugs nor vaccines. Therefore, it is now evident that non-medical approaches such as deep learning, data mining, expert system, software agents, and other artificial intelligence techniques are urgently needed to combat the pandemic, provide alternative solutions to alleviate the huge burden on the limited health care systems available around the world and curtail the future outbreak of the COVID-19 pandemic. Specifically, deep learning (DL) techniques evolved from machine learning (ML) concepts over a period of time and have been amply embraced in many real-life applications because of its unique nature and features for solving problems. Moreover, it is a powerful method of data exploration, and more importantly, has outperformed human efforts in several areas such as computer vision and health-related applications. Therefore, DL can be employed for combating and mitigating the proliferation of COVID-19 virus among humans. This chapter introduces the concept of deep learning and its potentials for combating the current spread COVID-19 pandemic and mitigating future outbreaks, discussed ongoing efforts of deep learning as one of the non-clinical approaches to alleviate the spread and curtail the further outbreak COVID-19 pandemic as well as the challenges of deep learning in combating COVID-19 pandemic and future directions.

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Author information

Authors and Affiliations

  1. The Federal University of Technology, Akure, Nigeria

    Oluwafemi A. Sarumi

  2. University of Nantes, Nantes, France

    Ons Aouedi

  3. Federal University Kashere, Gombe State, Nigeria

    Lawan J. Muhammad

Authors
  1. Oluwafemi A. Sarumi
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  2. Ons Aouedi
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  3. Lawan J. Muhammad
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Corresponding author

Correspondence to Oluwafemi A. Sarumi .

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Editors and Affiliations

  1. Department of Computer Science & Engineering, Aditya Institute of Technology and Management (AITAM) (An Autonomous Institution), Srikakulam, India

    Janmenjoy Nayak

  2. Department of Computer Application, Veer Surendra Sai University of Technology, Burla, India

    Bighnaraj Naik

  3. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

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Sarumi, O.A., Aouedi, O., Muhammad, L.J. (2022). Potential of Deep Learning Algorithms in Mitigating the Spread of COVID-19. In: Nayak, J., Naik, B., Abraham, A. (eds) Understanding COVID-19: The Role of Computational Intelligence. Studies in Computational Intelligence, vol 963. Springer, Cham. https://doi.org/10.1007/978-3-030-74761-9_10

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