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.
Highlights
The COVID-19 pandemic which was first reported in Wuhan, which is situated withinChina’s Hubei province, in December 2019 has affected, at the time of writing this article, 213 countries with confirmed cases totalling 10,614,903 recovered cases totalling 5,824,883 and death cases standing at an estimated 514,626 [1]
Contact tracing through big data is a very important domain that can assist with disease outbreaks, using different sources of data, e.g., posts with meta-data and tags used on social media, passenger lists, smartcards to metro, logs of a vehicle that people travel with and the use of credit card are all valuable sources of data
We provided a review of models of nature-inspired computing, artificial intelligence and big data for contact tracing
Summary
The COVID-19 pandemic which was first reported in Wuhan, which is situated within. The rapid increase in the number of newly confirmed COVID-19 presents a worrying situation for governments and public health workers These worries are predicated on the possibility of the number of infected cases overwhelming the healthcare infrastructure, especially the number of available beds, ventilators and personal protection equipment (PPE), as well as medical personnel required to attend to the incidence of COVID-19. It is projected that the state of healthcare infrastructure in developing countries like South Africa exposes such healthcare systems therein to being overwhelmed if an exponential increase in the number of COVID-19 cases is experienced [5,8,9] This understanding and the need to manage the rate of infections across the globe culminated in the lockdown of various economies, thereby resulting in movement restrictions, social distancing, deployment of rapid testing platforms and contact tracing. The contribution of this paper is as follows: (1) to identify the current AI-based interventions for the screening and diagnosis of COVID-19 pandemic cases and its use in contact tracing and case detection; (2) to explore the use of big data for contact tracing and case detection; and (3) to ascertain the possible combination of AI models and big data analytics with nature-inspired computing (NIC) to enhance the accuracy of the detection of COVID-19 cases
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