COVID-19 Deaths Previsions With Deep Learning Sequence Prediction

Bouhamed, Heni

doi:10.4018/ijbdah.20200701.oa1

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…Results show that the spread severity will intensify in this short term by 17.1%, and the average death cases will increase by 8.3%. From Table 2, we can see that the only study that used the LSTM method was the study of Bouhamed (Bouhamed, 2020), which predicted COVID-19 in several countries, including Iraq. His results for the case of Iraq were not discussed, while he observed the best prediction obtained in France.…”

Section: Worldometer Websitementioning

confidence: 99%

Bayesian Deep Learning Applied to LSTM Models for Predicting COVID-19 Confirmed Cases in Iraq

Hassan

Ahmed

2023

SJUOZ

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The COVID-19 pandemic has had a huge impact on populations around the world and has caused critical problems to medical systems. With the increasing number of COVID-19 infections, research has focused on forecasting the confirmed cases to make the right medical decisions. Despite the huge number of studies conducted to forecast the COVID-19 patients, the use of Deep Learning (DL) and Bayesian DL models are limited in this field in Iraq. Therefore, this research aims to predict the confirmed cases of COVID-19 in Iraq using classical DL models such as, Long-Short-Term-Memory (LSTM) and Bayesian LSTM models. In this study, Bayesian Deep Learning (BDL) using LSTM models was used to predict COVID-19 confirmed cases in Iraq. The motivation behind using BDL models is that they are capable to quantify model uncertainty and provide better results without overfitting or underfitting. A Monte Carlo (MC) Dropout, which is an approximation method, is added to the Bayesian-LSTM to create numerous predictions for each instance and evaluate epistemic uncertainty. To evaluate the performance of our proposed models, four evaluation measures (MSE, RMSE, R2, MAE) were used. Experimental results showed that the proposed models were efficient and provided an R2 of 0.93 and 0.92, for vanilla LSTM and Bayesian-LSTM, respectively. Furthermore, the two proposed models were optimized using ADAM and SGD optimizers, with the results revealing that optimizing with ADAM provided more accurate results. Thus, we believe that these models may assist the government in making critical decisions based on short-term predictions of confirmed cases in Iraq.

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Section: Worldometer Websitementioning

confidence: 99%