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.
