Abstract
As the world battles the COVID-19 pandemic, artificial intelligence, epidemiological analysis and novel sensing technologies can play key roles in mitigating the impacts of this unprecedented global crisis. This paper reviews how the domains of deep learning, numerical modelling and bio-sensing have rapidly responded with solutions to this grim pandemic scenario through the development of new methods for early detection and forecasting of the impacts of the COVID-19 virus. The recent applications in the area of deep learning-based computer vision and image analysis tools are discussed which have emerged with promising early COVID-19 detection methods using clinical chest imagery. In addition, a variety of predictive models developed in the current year for estimating the transmission, infection and mortality rates due the novel coronavirus are described which provide crucial information for determining social and governmental measures for containing the viral outbreak. Finally, state-of-the-art clinical test methodologies using nano-level sensors for point-of-care testing practices are highlighted which may enable rapid and accurate diagnosis of the COVID-19 infections. Overall, this paper brings forward some of the most advanced technologies available today and analyses how these may help mankind in defence of the survival challenges posed by the COVID-19 virus. In order to allow readers to further explore the concepts presented in this paper, an open repository named Coro-Lib containing implementations of the computational methods reviewed in this paper is created and described at the end.
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Kar, A., Kar, A. (2021). Deep Learning, Predictive Modelling and Nano/Bio-Sensing Technologies for Mitigation of the COVID-19 Pandemic. In: Balas, V.E., Hassanien, A.E., Chakrabarti, S., Mandal, L. (eds) Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing. Lecture Notes on Data Engineering and Communications Technologies, vol 62. Springer, Singapore. https://doi.org/10.1007/978-981-33-4968-1_1
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