Survey of COVID-19 Prediction Models and Their Limitations

Mohammad Ennab; Hamid Mcheick

doi:doi:10.11648/j.ijiis.20221102.11

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Survey of COVID-19 Prediction Models and Their Limitations

Mohammad Ennab, Hamid Mcheick

Published in International Journal of Intelligent Information Systems (Volume 11, Issue 2)

Received: 19 December 2021 Accepted: 11 April 2022 Published: 20 April 2022

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Abstract

COVID-19 pandemic has been spreading globally and has been influencing the daily life of human beings in addition to the economies of most countries around the globe. Early and accurate detection of COVID-19 coronavirus is crucial to prevent and control its outbreak using medical treatment and timely quarantine. The daily massive increases in the cases of COVID-19 patients worldwide and the limited solutions of the available diagnosing techniques have resulted in difficulties in pointing out the presence of the disease. Wherefore, the necessity arises to find other alternatives by leveraging the artificial intelligence (AI) models which create intelligent entities that have demonstrated themselves particularly successful due to their spectacular innovations in video processing and image, in addition to their highly accurate projection models. This survey contributes to studying the state of the art of the AI models that have been fighting against the COVID-19, highlighting the limitations that are significant and present noteworthy barriers to struggle with a pandemic, and recommends the trends for the incoming research on the pandemic.

Published in	International Journal of Intelligent Information Systems (Volume 11, Issue 2)
DOI	10.11648/j.ijiis.20221102.11
Page(s)	14-21
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Deep Learning, COVID-19, Prediction, Explainability

References

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APA Style

Mohammad Ennab, Hamid Mcheick. (2022). Survey of COVID-19 Prediction Models and Their Limitations. International Journal of Intelligent Information Systems, 11(2), 14-21. https://doi.org/10.11648/j.ijiis.20221102.11

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ACS Style

Mohammad Ennab; Hamid Mcheick. Survey of COVID-19 Prediction Models and Their Limitations. Int. J. Intell. Inf. Syst. 2022, 11(2), 14-21. doi: 10.11648/j.ijiis.20221102.11

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AMA Style

Mohammad Ennab, Hamid Mcheick. Survey of COVID-19 Prediction Models and Their Limitations. Int J Intell Inf Syst. 2022;11(2):14-21. doi: 10.11648/j.ijiis.20221102.11

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@article{10.11648/j.ijiis.20221102.11,
  author = {Mohammad Ennab and Hamid Mcheick},
  title = {Survey of COVID-19 Prediction Models and Their Limitations},
  journal = {International Journal of Intelligent Information Systems},
  volume = {11},
  number = {2},
  pages = {14-21},
  doi = {10.11648/j.ijiis.20221102.11},
  url = {https://doi.org/10.11648/j.ijiis.20221102.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20221102.11},
  abstract = {COVID-19 pandemic has been spreading globally and has been influencing the daily life of human beings in addition to the economies of most countries around the globe. Early and accurate detection of COVID-19 coronavirus is crucial to prevent and control its outbreak using medical treatment and timely quarantine. The daily massive increases in the cases of COVID-19 patients worldwide and the limited solutions of the available diagnosing techniques have resulted in difficulties in pointing out the presence of the disease. Wherefore, the necessity arises to find other alternatives by leveraging the artificial intelligence (AI) models which create intelligent entities that have demonstrated themselves particularly successful due to their spectacular innovations in video processing and image, in addition to their highly accurate projection models. This survey contributes to studying the state of the art of the AI models that have been fighting against the COVID-19, highlighting the limitations that are significant and present noteworthy barriers to struggle with a pandemic, and recommends the trends for the incoming research on the pandemic.},
 year = {2022}
}

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TY  - JOUR
T1  - Survey of COVID-19 Prediction Models and Their Limitations
AU  - Mohammad Ennab
AU  - Hamid Mcheick
Y1  - 2022/04/20
PY  - 2022
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DO  - 10.11648/j.ijiis.20221102.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
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PB  - Science Publishing Group
SN  - 2328-7683
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AB  - COVID-19 pandemic has been spreading globally and has been influencing the daily life of human beings in addition to the economies of most countries around the globe. Early and accurate detection of COVID-19 coronavirus is crucial to prevent and control its outbreak using medical treatment and timely quarantine. The daily massive increases in the cases of COVID-19 patients worldwide and the limited solutions of the available diagnosing techniques have resulted in difficulties in pointing out the presence of the disease. Wherefore, the necessity arises to find other alternatives by leveraging the artificial intelligence (AI) models which create intelligent entities that have demonstrated themselves particularly successful due to their spectacular innovations in video processing and image, in addition to their highly accurate projection models. This survey contributes to studying the state of the art of the AI models that have been fighting against the COVID-19, highlighting the limitations that are significant and present noteworthy barriers to struggle with a pandemic, and recommends the trends for the incoming research on the pandemic.
VL  - 11
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Author Information

Mohammad Ennab

Computer Science and Mathematics Department, University of Québec, Chicoutimi, Canada
Hamid Mcheick

Computer Science and Mathematics Department, University of Québec, Chicoutimi, Canada

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APA Style

Mohammad Ennab, Hamid Mcheick. (2022). Survey of COVID-19 Prediction Models and Their Limitations. International Journal of Intelligent Information Systems, 11(2), 14-21. https://doi.org/10.11648/j.ijiis.20221102.11

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ACS Style

Mohammad Ennab; Hamid Mcheick. Survey of COVID-19 Prediction Models and Their Limitations. Int. J. Intell. Inf. Syst. 2022, 11(2), 14-21. doi: 10.11648/j.ijiis.20221102.11

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AMA Style

Mohammad Ennab, Hamid Mcheick. Survey of COVID-19 Prediction Models and Their Limitations. Int J Intell Inf Syst. 2022;11(2):14-21. doi: 10.11648/j.ijiis.20221102.11

Copy | Download

@article{10.11648/j.ijiis.20221102.11,
  author = {Mohammad Ennab and Hamid Mcheick},
  title = {Survey of COVID-19 Prediction Models and Their Limitations},
  journal = {International Journal of Intelligent Information Systems},
  volume = {11},
  number = {2},
  pages = {14-21},
  doi = {10.11648/j.ijiis.20221102.11},
  url = {https://doi.org/10.11648/j.ijiis.20221102.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20221102.11},
  abstract = {COVID-19 pandemic has been spreading globally and has been influencing the daily life of human beings in addition to the economies of most countries around the globe. Early and accurate detection of COVID-19 coronavirus is crucial to prevent and control its outbreak using medical treatment and timely quarantine. The daily massive increases in the cases of COVID-19 patients worldwide and the limited solutions of the available diagnosing techniques have resulted in difficulties in pointing out the presence of the disease. Wherefore, the necessity arises to find other alternatives by leveraging the artificial intelligence (AI) models which create intelligent entities that have demonstrated themselves particularly successful due to their spectacular innovations in video processing and image, in addition to their highly accurate projection models. This survey contributes to studying the state of the art of the AI models that have been fighting against the COVID-19, highlighting the limitations that are significant and present noteworthy barriers to struggle with a pandemic, and recommends the trends for the incoming research on the pandemic.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Survey of COVID-19 Prediction Models and Their Limitations
AU  - Mohammad Ennab
AU  - Hamid Mcheick
Y1  - 2022/04/20
PY  - 2022
N1  - https://doi.org/10.11648/j.ijiis.20221102.11
DO  - 10.11648/j.ijiis.20221102.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 14
EP  - 21
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20221102.11
AB  - COVID-19 pandemic has been spreading globally and has been influencing the daily life of human beings in addition to the economies of most countries around the globe. Early and accurate detection of COVID-19 coronavirus is crucial to prevent and control its outbreak using medical treatment and timely quarantine. The daily massive increases in the cases of COVID-19 patients worldwide and the limited solutions of the available diagnosing techniques have resulted in difficulties in pointing out the presence of the disease. Wherefore, the necessity arises to find other alternatives by leveraging the artificial intelligence (AI) models which create intelligent entities that have demonstrated themselves particularly successful due to their spectacular innovations in video processing and image, in addition to their highly accurate projection models. This survey contributes to studying the state of the art of the AI models that have been fighting against the COVID-19, highlighting the limitations that are significant and present noteworthy barriers to struggle with a pandemic, and recommends the trends for the incoming research on the pandemic.
VL  - 11
IS  - 2
ER  -

Copy | Download