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Sentimental Analysis and Deep Learning pp 97–111Cite as

A Deep Learning Approach to Analyze the Propagation of Pandemic in America

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1408))

Abstract

The new Coronavirus Disease 2019 (COVID-19) opened new and significant challenges for the research community. Deep learning (DL) models could be used to extract important information from continuously generated data to detect and predict the COVID-19 epidemic growth by continuously monitoring it. Together with the next-generation fog computing (FC) framework, the strategies could be designed to help and manage the spread of the virus in a specific region effectively. Inspired by future Internet technologies, we propose an algorithm that applies a mathematical model using DL to predict and analyze the growth and spread of the COVID-19 in America. We also proposed an FC platform to predict real-time data gathered from patients in America’s geographical location. The DL-based prediction technique to be utilized in remote Fog Nodes (FNs) to make a more accurate prediction in human networks. Finally, we delineate some research opportunities, as well as preparation bases for practical applications. The simulation results confirm the model’s efficiency and precision in America and the countries most affected by the epidemic. The proposed model could help the related governments apply needful actions to control the spread of the pandemic.

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Author information

Authors and Affiliations

  1. Faculty of Engineering, Information Technology and Communication, Universidad Nacional de Chimborazo, Riobamba, Ecuador

    Paola G. Vinueza-Naranjo

  2. Faculty of Engineering, Telecommunication, Pontificia Universidad Católica del Ecuador, Quito, Ecuador

    Angel F. Vinueza-Naranjo

  3. Faculty of Engineering, Telecommunication, Federal University of Pará, Belem, Brazil

    Hieda A. Nascimento-Silva

Authors
  1. Paola G. Vinueza-Naranjo
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  2. Angel F. Vinueza-Naranjo
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  3. Hieda A. Nascimento-Silva
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Corresponding author

Correspondence to Paola G. Vinueza-Naranjo .

Editor information

Editors and Affiliations

  1. Institute of Engineering, Tribhuvan University, Pulchowk Campus, Lalitpur, Nepal

    Subarna Shakya

  2. Intelligent Systems Research Centre, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Songkla University, Songkhla, Thailand

    Sinchai Kamolphiwong

  4. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Ke-Lin Du

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Vinueza-Naranjo, P.G., Vinueza-Naranjo, A.F., Nascimento-Silva, H.A. (2022). A Deep Learning Approach to Analyze the Propagation of Pandemic in America. In: Shakya, S., Balas, V.E., Kamolphiwong, S., Du, KL. (eds) Sentimental Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1408. Springer, Singapore. https://doi.org/10.1007/978-981-16-5157-1_8

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