A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries

Abstract

Accurate prediction models have become the first goal for aiding pandemic-related decisions. Modeling and predicting the number of new active cases and deaths are important steps for anticipating and controlling COVID-19 outbreaks. The aim of this research was to develop an accurate prediction system for the COVID-19 pandemic that can predict the numbers of active cases and deaths in the Gulf countries of Saudi Arabia, Oman, the United Arab Emirates (UAE), Kuwait, Bahrain, and Qatar. The novelty of the proposed approach is that it uses an advanced prediction model—the bidirectional long short-term memory (Bi-LSTM) network deep learning model. The datasets were collected from an available repository containing updated registered cases of COVID-19 and showing the global numbers of active COVID-19 cases and deaths. Statistical analyses (e.g., mean square error, root mean square error, mean absolute error, and Spearman’s correlation coefficient) were employed to evaluate the results of the adopted Bi-LSTM model. The Bi-LSTM results based on the correlation metric gave predicted confirmed COVID-19 cases of 99.67%, 99.34%, 99.94%, 99.64%, 98.95%, and 99.91% for Saudi Arabia, Oman, the UAE, Kuwait, Bahrain, and Qatar, respectively, while testing the Bi-LSTM model for predicting COVID-19 mortality gave accuracies of 99.87%, 97.09%, 99.53%, 98.71%, 95.62%, and 99%, respectively. The Bi-LSTM model showed significant results using the correlation metric. Overall, the Bi-LSTM model demonstrated significant success in predicting COVID-19. The Bi-LSTM-based deep learning network achieves optimal prediction results and is effective and robust for predicting the numbers of active cases and deaths from COVID-19 in the studied Gulf countries.

Global Increase in Breast Cancer Incidence: Risk Factors and Preventive Measures

“…The main reason is the lack of accurate information and loop in utilizing the availability of these facilities. In addition, currently, the COVID-19 pandemic around the world caused health system or screening programs closures, delay in diagnosis or treatment availability, and increases in advanced-stage diagnoses and mortality [ 30 – 35 ]. The present review article will summarize the current status of breast cancer morbidity and mortality, major risk factors behind, and possible strategies for the prevention of breast cancer risk.…”

Design of Branch Definition Algorithm for Top- k Inverse Queries for Image Processing

“…Due to the buffering used, the other branch definition algorithms appear to be very efficient in terms of S -regulated I / O . It is worth noting that both other algorithms outperform the RTA in terms of I / O calls by W , which indicates that the application of scoring bounds plays a role [ 26 , 27 ].…”

miRNA-448 Regulates the Development of Glioblastoma (GBM) by Regulating Rho-Associated Protein Kinase 1

“…In general, the mean survival time of GBM patients after surgery, chemotherapy, and radiotherapy is approximately 14 months, and most GBM patients have a poor prognosis [ 3 ]. Today, targeted therapy and biological therapy have become the new direction of GBM treatment [ 4 , 5 ] and also help to prolong the survival of patients. Therefore, exploring new therapeutic targets is of great significance for GBM patients.…”