Office of the Deputy Vice Chancellor: Research, Innovation and Engagement, Central University of Technology, Bloemfontein 9301, South Africa.
Centre for Sustainable Smart Cities 4.0, Faculty of Engineering, Built Environment and Information Technology, Central University of Technology, Bloemfontein 9301, South Africa.
Int J Environ Res Public Health. 2020 Jul 24;17(15):5330. doi: 10.3390/ijerph17155330.
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.
2019 年新型冠状病毒（COVID-19）的出现被宣布为大流行，已经蔓延到全球 210 个国家。它对卫生系统以及当代社会的经济、教育和社会方面都产生了重大影响。随着传播率的增加，利益相关者之间已经发展出各种合作方法，以开发创新的方法，在适当的速度下对人类进行 COVID-19 病例的筛查、检测和诊断。此外，强调了与第四次工业革命技术相关的计算模型在实现这一目标方面的效用。然而，在 COVID-19 病例的检测和预测准确性以及感染人员接触者的追踪方面存在差距。本文回顾了可以采用的计算模型，以提高检测和预测 COVID-19 大流行病例的性能。我们专注于可以在当前大流行中采用的大数据、人工智能 (AI) 和受自然启发的计算 (NIC) 模型。审查表明，人工智能模型已用于 COVID-19 的病例检测。同样，大数据平台也已用于追踪接触者。然而，在医学问题的特征选择方面表现出良好性能的受自然启发的计算 (NIC) 模型尚未在当前 COVID-19 大流行中用于病例检测和接触者追踪。这项研究对从业者和研究人员都具有重要意义，因为它阐明了 NIC 在准确检测大流行病例和优化接触者追踪方面的潜力。
