Abstract
The spread of COVID-19 worldwide continues despite multidimensional efforts to curtail its spread and provide treatment. Efforts to contain the COVID-19 pandemic have triggered partial or full lockdowns across the globe. This paper presents a novel framework that intelligently combines machine learning models and the Internet of Things (IoT) technology specifically to combat COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology by interacting with a population and its environment to curtail the COVID-19 pandemic. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store, and analyze data using machine learning algorithms. These algorithms can detect, prevent, and trace the spread of COVID-19 and provide a better understanding of the disease in smart cities. Similarly, the study outlined case studies on the application of machine learning to help fight against COVID-19 in hospitals worldwide. The framework proposed in the study is a comprehensive presentation on the major components needed to integrate the machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a pointer for generating research interests that would yield outcomes capable of been integrated to form an improved framework.
Highlights
The novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused unprecedented numbers of deaths from coronavirus disease 2019 (COVID-19) worldwide
To address the multiple dimensional challenges posed by the COVID-19 pandemic, as outlined in Section 1, we propose that a framework is required within the smart city context to allow decision makers to make the crucial decisions on the best ways to combat COVID19 from multiple dimensions
The findings from this study have showed that handcrafted and manually driven mechanisms for managing COVID-19 remain ineffective as the outbreak has been overwhelming, defying such mechanisms
Summary
The novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused unprecedented numbers of deaths from coronavirus disease 2019 (COVID-19) worldwide. Genomic sequences of the early isolates of SARS-CoV-2 from infected patients in Wuhan showed over 88% nucleotide homology with two bat-like SARS coronaviruses, which pointed strongly towards the zoonotic source with bats serving as reservoir hosts of the SARSCoV-2 [4]. SARS-CoV-2 is a droplet borne pathogen that spreads by contact with humans when they are exposed to oral or nasal secretions of clinically symptomatically or asymptomatically infected persons [5]. An attempt is made to present fundamental knowledge about the disease COVID19 caused by the novel coronavirus SARS-CoV-2, with background information and clinical features which are relevant for the implementation of the proposed framework. Several observers have attributed the low incidence rate of COVID-19 in sub-Saharan Africa to underdiagnosis, probably due to inadequate molecular diagnostic capacity, but variation in the genetics, strains, viral protein mutations, and host immune response could have contributed to SARS-CoV-2 virulence and pathogenesis [23]
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