Hybrid Deep Learning Algorithm for Forecasting SARS-CoV-2 Daily Infections and Death Cases

Alqahtani, Fehaid; Abotaleb, Mostafa; Kadi, Ammar; Makarovskikh, Tatiana; Potoroko, Irina; Alakkari, Khder Mohammed; Badr, Amr

doi:10.3390/axioms11110620

Cited by 8 publications

(4 citation statements)

References 41 publications

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“…During the COVID-19 pandemic, a substantial effort has been made to develop ML models to either predict case numbers from epidemiological data [ 3 , 4 , 5 , 6 , 7 ] or to classify SARS-CoV-2 sequences using genomic data [ 28 , 29 , 30 , 31 ], but in most situations, there was no attempt to explain the output of these models. With recent progress in model explainability, ML models are less and less considered as black boxes, and explaining them is especially important in epidemiology and biology.…”

Section: Discussionmentioning

confidence: 99%

“…Since the publication of the seminal work of the late Sir Robert May [ 1 ], most of modern epidemiology aims at predicting the severity of viral outbreaks based on the number of individuals who are susceptible, infected, and recovered (or dead) in a population—that is, based on epidemiological data, as has been the case, for instance, during the COVID-19 pandemic, caused by the SARS-CoV-2 virus [ 2 ]. Complementary approaches have nonetheless resorted to machine learning (ML) to improve predictions, but these applications mainly focused on the same kind of epidemiological data [ 3 , 4 , 5 , 6 , 7 ] or on image processing to diagnose the disease [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Algorithms Associate Case Numbers with SARS-CoV-2 Variants Rather Than with Impactful Mutations

Vilain

Aris‐Brosou

2023

Viruses

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During the SARS-CoV-2 pandemic, much effort has been geared towards creating models to predict case numbers. These models typically rely on epidemiological data, and as such overlook viral genomic information, which could be assumed to improve predictions, as different variants show varying levels of virulence. To test this hypothesis, we implemented simple models to predict future case numbers based on the genomic sequences of the Alpha and Delta variants, which were co-circulating in Texas and Minnesota early during the pandemic. Sequences were encoded, matched with case numbers at a future time based on collection date, and used to train two algorithms: one based on random forests and one based on a feed-forward neural network. While prediction accuracies were ≥93%, explainability analyses showed that the models were not associating case numbers with mutations known to have an impact on virulence, but with individual variants. This work highlights the necessity of gaining a better understanding of the data used for training and of conducting explainability analysis to assess whether model predictions are misleading.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms Associate Case Numbers with SARS-CoV-2 Variants Rather Than with Impactful Mutations

Vilain

Aris‐Brosou

2023

Viruses

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“…As the COVID-19 pandemic (global pandemic) becomes a global pandemic (pandemic), it is necessary to conduct an epidemiological investigation in real time to provide the public with a clear direction to fight the infection. According to [ 3 ], the authors utilized a combined CNN-LTSM model using a time-series dataset to predict the confirmed cases of COVID-19 [ 33 ]. The CNN-LTSM encoder-decoder technique helps significantly boost prediction performance [ 34 ].…”

Section: Literature Surveymentioning

confidence: 99%

Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images

Gupta

Srinivasulu

Hiran

et al. 2022

Interdisciplinary Perspectives on Infectious Diseases

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COVID-19 has sparked a global pandemic, with a variety of inflamed instances and deaths increasing on an everyday basis. Researchers are actively increasing and improving distinct mathematical and ML algorithms to forecast the infection. The prediction and detection of the Omicron variant of COVID-19 brought new issues for the health fraternity due to its ubiquity in human beings. In this research work, two learning algorithms, namely, deep learning (DL) and machine learning (ML), were developed to forecast the Omicron virus infections. Automatic disease prediction and detection have become crucial issues in medical science due to rapid population growth. In this research study, a combined Extended CNN-RNN research model was developed on a chest CT-scan image dataset to predict the number of +ve and −ve cases of Omicron virus infections. The proposed research model was evaluated and compared against the existing system utilizing a dataset of 16,733-sample training and testing CT-scan images collected from the Kaggle repository. This research article aims to introduce a combined ML and DL technique based on the combination of an Extended Convolutional Neural Network (ECNN) and an Extended Recurrent Neural Network (ERNN) to diagnose and predict Omicron virus-infected cases automatically using chest CT-scan images. To overcome the drawbacks of the existing system, this research proposes a combined research model that is ECNN-ERNN, where ECNN is used for the extraction of deep features and ERNN is used for exploration using extracted features. A dataset of 16,733 Omicron computer tomography images was used as a pilot assessment for this proposed prototype. The investigational experiment results show that the projected prototype provides 97.50% accuracy, 98.10% specificity, 98.80% of AUC, and 97.70% of F1-score. To the last, the study outlines the advantages being offered by the proposed model with respect to other existing models by comparing different parameters of validation such as accuracy, error rate, data size, time complexity, and execution time.

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“…In "Hybrid Deep Learning Algorithm for Forecasting SARS-CoV-2 Daily Infections and Death Cases" by Fehaid Alqahtani, Mostafa Abotaleb, Ammar Kadi, Tatiana Makarovskikh, Irina Potoroko, Khder Alakkari, and Amr Badr [8], the authors used a hybrid deep learning algorithm to predict new cases of infection which is crucial for authorities to get ready for early handling of the virus spread. The hybrid deep learning method was used to improve the parameters of long short-term memory (LSTM).…”

mentioning

confidence: 99%

Various Deep Learning Algorithms in Computational Intelligence

Montiel

2023

Axioms

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Hybrid Deep Learning Algorithm for Forecasting SARS-CoV-2 Daily Infections and Death Cases

Cited by 8 publications

References 41 publications

Machine Learning Algorithms Associate Case Numbers with SARS-CoV-2 Variants Rather Than with Impactful Mutations

Machine Learning Algorithms Associate Case Numbers with SARS-CoV-2 Variants Rather Than with Impactful Mutations

Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images

Various Deep Learning Algorithms in Computational Intelligence

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