ABSTRACT
In this paper, we reviewed the Fourth Industrial Revolution (4IR) technologies applied to waves of the coronavirus disease (COVID-19). COVID-19 is an existential threat that has resulted in an unprecedented loss of lives, disruption of flight schedules, shutdown of businesses and much more. Though several researchers have highlighted the enormous benefits of 4IR technologies in containing the COVID-19 pandemic, the recent waves of the pandemic call for a thorough review of these technological interventions. The cyber-physical space has had its share of the COVID-19 pandemic effect, and through this review, we highlight the salient issues to help policy formulation towards managing the impact of subsequent COVID-19 waves within such environments. Hence, the purpose of this paper is to review the application of 4IR technologies during the COVID-19 pandemic waves and to highlight their shortcomings. Recent research articles were sourced from an online repository and thoroughly reviewed to highlight 4IR technology applications, innovations, shortcomings and multi-sector challenges. The outcome of this review indicates that the second wave of the pandemic resulted in a lower proportion of patients requiring invasive mechanical ventilation and a lower rate of thrombotic events. In addition, it was revealed that the delay between ICU admissions and tracheal intubation was longer in the second wave in the health care sector. Again, the review suggests that 4IR technologies have been utilized across all the sectors including education, businesses, society, manufacturing, healthcare, agriculture and mining. Businesses have revised their service delivery models to include 4IR technologies and avoid physical contacts. In society, digital certificates, among other digital platforms, have been utilized to assist with the movements of persons who have been vaccinated. Manufacturing concerns have also utilized robots in manufacturing to reduce human-to-human physical contact. The mining sector has automated their work processes, utilising smart boots to prevent infection, smart health bands and smart disinfection tunnels or walkthrough sanitization gates in the mining work environment. However, the identified challenges of implementing 4IR technologies include low-skilled workers, data privacy issues, data analysis poverty, data management issues and many more. The boom in 4IR technologies calls for intense legislation on sweeping data privacy for regulated tech companies. These findings hold salient implications for policy formulation towards tackling future pandemic outbreaks.
ABSTRACT
The emergence of the 2019 novel coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide. It has had a significant impact on health systems and economic, educational and social facets of contemporary society. As the rate of transmission increases, various collaborative approaches among stakeholders to develop innovative means of screening, detecting and diagnosing COVID-19's cases among human beings at a commensurate rate have evolved. Further, the utility of computing models associated with the fourth industrial revolution technologies in achieving the desired feat has been highlighted. However, there is a gap in terms of the accuracy of detection and prediction of COVID-19 cases and tracing contacts of infected persons. This paper presents a review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases. We focus on big data, artificial intelligence (AI) and nature-inspired computing (NIC) models that can be adopted in the current pandemic. The review suggested that artificial intelligence models have been used for the case detection of COVID-19. Similarly, big data platforms have also been applied for tracing contacts. However, the nature-inspired computing (NIC) models that have demonstrated good performance in feature selection of medical issues are yet to be explored for case detection and tracing of contacts in the current COVID-19 pandemic. This study holds salient implications for practitioners and researchers alike as it elucidates the potentials of NIC in the accurate detection of pandemic cases and optimized contact tracing.
Subject(s)
Artificial Intelligence , Big Data , Computer Simulation , Contact Tracing , Betacoronavirus , COVID-19 , Coronavirus Infections , Humans , Pandemics/prevention & control , Pneumonia, Viral , SARS-CoV-2ABSTRACT
The emergence of the 2019 Novel Coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide. It has significantly affected the health system, the economic, educational and social facets of contemporary society. As the rate of transmission continues to rise, various collaborative approaches among stakeholders to develop innovative means of screening and detecting COVID-19 cases among human beings at a commensurate rate has been observed. In addition, the utility of computing models associated with the 4 th Industrial revolution technologies in achieving the desired feat has been highlighted. However, there is a gap in terms of accuracy of detection and prediction of COVID-19 cases and tracing of contacts. This paper presents a review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases. We focus on big data, artificial intelligence and Nature-Inspired Computing models that can be adopted in the current pandemic. The review suggested that Nature-Inspired Computing models have demonstrated good performance in feature selection of medical issues. Additionally, contact tracing using big data analytics should be explored in pandemic related cases such as COVID-19.