COVID-19: The Dynamics of Quality of Life (QOL) and Artificial Intelligence (AI)

Authors

  • Bongs Lainjo Cybermatic International, Canada

DOI:

https://doi.org/10.51415/ajims.v3i1.908

Keywords:

Covid-19, quality of life, strategies, lessons learned, artificial intelligence

Abstract

The objective of the study is to conduct an exploratory review of the COVID-19 pandemic by focusing on the theme of COVID-19 pandemic morbidity and mortality considering the dynamics of Artificial Intelligence and Quality of life (QOL). The methods used in this research paper include a review of literature, anecdotal evidence, and reports on the morbidity of COVID-19, including the scope of its devastating effects in different countries such as the US, Africa, UK, China, and Brazil, among others. The findings of this study suggested that the devastating effects of the Coronavirus are felt across different vulnerable populations. These include the elderly, front-line workers, marginalised communities, visible minorities, and more. The challenge in Africa is especially daunting because of inadequate infrastructure, financial, and human resources, among others. Besides, AI technology is being successfully used by scientists to enhance the development process of vaccines and drugs. However, its usage in other stages of the pandemic has not been adequately explored. Ultimately, it has been concluded that the effects of the COVID-19 are producing unprecedented and catastrophic outcomes in many countries. With a few exceptions, the common and current intervention approach is driven by many factors, including the compilation of relevant reliable and compelling data sets. On a positive note, the compelling trailblazing and catalytic contributions of AI towards the rapid discovery of COVID-19 vaccines are a good indication of future technological innovations and their effectiveness.

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Published

03-11-2021 — Updated on 15-11-2021

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How to Cite

Lainjo, B. (2021) “COVID-19: The Dynamics of Quality of Life (QOL) and Artificial Intelligence (AI)”, African Journal of Inter/Multidisciplinary Studies, 3(1), pp. 161–177. doi: 10.51415/ajims.v3i1.908.