Open AccessA critical review of emerging technologies for tackling COVID ‐19 pandemic
Author(s) -
Mbunge Elliot,
Akinnuwesi Boluwaji,
Fashoto Stephen G.,
Metfula Andile S.,
Mashwama Petros
Publication year - 2021
Publication title -
human behavior and emerging technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.823
H-Index - 8
ISSN - 2578-1863
DOI - 10.1002/hbe2.237
Subject(s) - pandemic , covid-19 , emerging technologies , big data , computer science , data science , telemedicine , the internet , risk analysis (engineering) , computer security , artificial intelligence , business , health care , medicine , data mining , political science , world wide web , disease , pathology , infectious disease (medical specialty) , law
COVID‐19 pandemic affects people in various ways and continues to spread globally. Researches are ongoing to develop vaccines and traditional methods of Medicine and Biology have been applied in diagnosis and treatment. Though there are success stories of recovered cases as of November 10, 2020, there are no approved treatments and vaccines for COVID‐19. As the pandemic continues to spread, current measures rely on prevention, surveillance, and containment. In light of this, emerging technologies for tackling COVID‐19 become inevitable. Emerging technologies including geospatial technology, artificial intelligence (AI), big data, telemedicine, blockchain, 5G technology, smart applications, Internet of Medical Things (IoMT), robotics, and additive manufacturing are substantially important for COVID‐19 detecting, monitoring, diagnosing, screening, surveillance, mapping, tracking, and creating awareness. Therefore, this study aimed at providing a comprehensive review of these technologies for tackling COVID‐19 with emphasis on the features, challenges, and country of domiciliation. Our results show that performance of the emerging technologies is not yet stable due to nonavailability of enough COVID‐19 dataset, inconsistency in some of the dataset available, nonaggregation of the dataset due to contrasting data format, missing data, and noise. Moreover, the security and privacy of people's health information is not totally guaranteed. Thus, further research is required to strengthen the current technologies and there is a strong need for the emergence of a robust computationally intelligent model for early differential diagnosis of COVID‐19.