A Novel Smart City Based Framework on Perspectives for application of Machine Learning in combatting COVID-19

  1. Absalom E. Ezugwu
  2. Ibrahim A.T. Hashem
  3. Olaide N. Oyelade
  4. Haruna Chiroma
  5. Mohammed A. Al-Garadi
  6. Idris N. Abdullahi
  7. Olumuyiwa Otegbeye
  8. Amit K. Shukla
  9. Mubarak Almutari

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Abstract

The spread of COVID-19 across the world continues as efforts are being made from multi-dimension to curtail its spread and provide treatment. The COVID-19 triggered partial and full lockdown across the globe in an effort to prevent its spread. COVID-19 causes serious fatalities with United States of America recording over 3,000 deaths within 24 hours, the highest in the world for a single day and as of October 2020 has recorded a total of 270,642 death toll. In this paper, we present a novel framework which intelligently combines machine learning models and internet of things (IoT) technology specific in combatting COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology in interacting with a population and its environment with the aim of curtailing COVID-19. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store and analyze data using machine learning algorithms. These algorithms are able to detect, prevent, and trace the spread of COVID-19, and provide better understanding of the virus in smart cities. Similarly, the study outlined case studies on the application of machine learning to help in the fight against COVID-19 in hospitals across the world. The framework proposed in the study is a comprehensive presentation on the major components needed for an integration of machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a point for generating research interests which will yield outcomes capable of been integrated to form an improved framework.

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  1. Version published to 10.1101/2020.05.18.20105577v4 on medRxiv
  2. Version published to 10.1155/2021/5546790
  3. Version published to 10.1101/2020.05.18.20105577v3 on medRxiv
  4. ScreenIT

    SciScore for 10.1101/2020.05.18.20105577: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  5. Version published to 10.1101/2020.05.18.20105577v2 on medRxiv
  6. Version published to 10.1101/2020.05.18.20105577v1 on medRxiv