Exploring Machine Learning in Healthcare and its Impact on the SARS-CoV-2 Outbreak


  • Dennie James Carleton University
  • Tanya James King's College London


SARS-CoV-2, COVID-19, Coronavirus, Artificial intelligence, Machine learning, Deep learning, Smart cities


Machine learning can be defined as a comprehensive range of tools utilized for recognizing patterns in data. Owing to its reliance on artificial intelligence in lieu of age-old, traditional methods, machine learning has established itself as an exceedingly quicker way of discerning patterns and trends from bulk data. The advanced system can even update itself on the availability of new data. This paper intends to elucidate different techniques involved in machine learning that have facilitated the prediction, detection, and restriction of infectious diseases in the past few decades. Moreover, in light of the unprecedented COVID-19 pandemic, such tools and techniques have been utilized extensively by smart cities to curb the proliferation of the SARS-CoV-2 virus. However, the strengths and weaknesses of this approach remain abstruse and therefore, this review also aims to evaluate the role of machine learning in the recent coronavirus outbreak.


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Author Biographies

Dennie James, Carleton University

Faculty of Engineering and Design, Carleton University, Ottawa, CANADA & BAIOTEQ, Saint John, CANADA

Tanya James, King's College London

Department of Social Science, Health and Medicine, King's College London, UK


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How to Cite

James, D., & James, T. (2021). Exploring Machine Learning in Healthcare and its Impact on the SARS-CoV-2 Outbreak . Asian Journal of Applied Science and Engineering, 10(1), 9-18. Retrieved from https://journals.abc.us.org/index.php/ajase/article/view/1191



Peer-reviewed Article