Neural Networks and Forecasting COVID-19

Dadyan, Eduard; Avetisyan, P.

doi:10.3103/s1060992x21030085

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…The potential of deep neural networks has been explored by numerous studies involving different areas related to the COVID-19 pandemic, such as for virus detection [ 10 – 12 ], classification of image exams such as X-Ray and computed tomography (CT) [ 13 , 14 ], and time-series forecasting [ 15 – 17 ]. Considering the use of artificial intelligence on different battlefronts against the COVID-19 pandemic, El-Rashidy et al [ 18 ] conducted a survey of the most important applications and found 5 types: diagnosis, transmission estimation, study of the characteristics of different populations and the effects of Covid-19, vaccine development and the study of other drugs, supporting applications.…”

Section: Related Workmentioning

confidence: 99%

Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural networks

et al. 2023

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This work aims to compare deep learning models designed to predict daily number of cases and deaths caused by COVID-19 for 183 countries, using a daily basis time series, in addition to a feature augmentation strategy based on Discrete Wavelet Transform (DWT). The following deep learning architectures were compared using two different feature sets with and without DWT: (1) a homogeneous architecture containing multiple LSTM (Long-Short Term Memory) layers and (2) a hybrid architecture combining multiple CNN (Convolutional Neural Network) layers and multiple LSTM layers. Therefore, four deep learning models were evaluated: (1) LSTM, (2) CNN + LSTM, (3) DWT + LSTM and (4) DWT + CNN + LSTM. Their performances were quantitatively assessed using the metrics: Mean Absolute Error (MAE), Normalized Mean Squared Error (NMSE), Pearson R, and Factor of 2. The models were designed to predict the daily evolution of the two main epidemic variables up to 30 days ahead. After a fine-tuning procedure for hyperparameters optimization of each model, the results show a statistically significant difference between the models’ performances both for the prediction of deaths and confirmed cases (p-value<0.001). Based on NMSE values, significant differences were observed between LSTM and CNN+LSTM, indicating that convolutional layers added to LSTM networks made the model more accurate. The use of wavelet coefficients as additional features (DWT+CNN+LSTM) achieved equivalent results to CNN+LSTM model, which demonstrates the potential of wavelets application for optimizing models, since this allows training with a smaller time series data.

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Section: Related Workmentioning

confidence: 99%

Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural networks

et al. 2023

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“…where, β 2 -similar β 1 the hyperparameter that controls the exponential decay rates of the sliding, and is taken as 0.999 as the most appropriate value for machine learning problems [39,40]. However, these slides are initialized as vectors close to zero (and the values will accumulate for a long time).…”

Section: Determination Of the Dataset And Neural Network Architecturesmentioning

confidence: 99%

COVID-19 Spread Forecasting, Mathematical Methods vs. Machine Learning, Moscow Case

et al. 2022

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To predict the spread of the new coronavirus infection COVID-19, the critical values of spread indicators have been determined for deciding on the introduction of restrictive measures using the city of Moscow as an example. A model was developed using classical methods of mathematical modeling based on exponential regression, the accuracy of the forecast was estimated, and the shortcomings of mathematical methods for predicting the spread of infection for more than two weeks. As a solution to the problem of the accuracy of long-term forecasts for more than two weeks, two models based on machine learning methods are proposed: a recurrent neural network with two layers of long short-term memory (LSTM) blocks and a 1-D convolutional neural network with a description of the choice of an optimization algorithm. The forecast accuracy of ML models was evaluated in comparison with the exponential regression model and one another using the example of data on the number of COVID-19 cases in the city of Moscow.

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Time-Series Data Prediction Using Johnson-Lindenstrauss Lemma, Fuzzy Logic, And Self Organizing Maps

Gopalan

2023

2023 2nd International Conference on Edge Computing and Applications (ICECAA)

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Neural Networks and Forecasting COVID-19

Cited by 3 publications

References 28 publications

Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural networks

Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural networks

COVID-19 Spread Forecasting, Mathematical Methods vs. Machine Learning, Moscow Case

Time-Series Data Prediction Using Johnson-Lindenstrauss Lemma, Fuzzy Logic, And Self Organizing Maps

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