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.

Growing consumerism and population worldwide raises concerns about society’s sustainability aspirations. This has led to calls for concerted efforts to shift from the linear economy to a circular economy (CE), which are gaining momentum globally. CE approaches lead to a zero-waste scenario of economic growth and sustainable development. These approaches are based on semi-scientific and empirical concepts with technologies enabling 3Rs (reduce, reuse, recycle) and 6Rs (reuse, recycle, redesign, remanufacture, reduce, recover). Studies estimate that the transition to a CE would save the world in excess of a trillion dollars annually while creating new jobs, business opportunities and economic growth. The emerging industrial revolution will enhance the symbiotic pursuit of new technologies and CE to transform extant production systems and business models for sustainability. This article examines the trends, availability and readiness of fourth industrial revolution (4IR or industry 4.0) technologies (for example, Internet of Things [IoT], artificial intelligence [AI] and nanotechnology) to support and promote CE transitions within the higher education institutional context. Furthermore, it elucidates the role of universities as living laboratories for experimenting the utility of industry 4.0 technologies in driving the shift towards CE futures. The article concludes that universities should play a pivotal role in engendering CE transitions.

No abstract

No abstract