ArticleProcess safety and environmental protection : transactions of the Institution of Chemical Engineers, Part B2021
Deep learning model for forecasting COVID-19 outbreak in Egypt.
Article in Process safety and environmental protection : transactions of the Institution of Chemical Engineers, Part B, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Spatio-temporal epidemic forecasting with graph-based transformer.International journal of health geographics · 2026Article
- A Systematic Review of the Outcomes of Utilization of Artificial Intelligence Within the Healthcare Systems of the Middle East: A Thematic Analysis of Findings.Health science reports · 2024Review
- Spatio-temporal analysis of COVID-19 lockdown effect to survive in the US counties using ANN.Scientific reports · 2024Article
- AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods.Osong public health and research perspectives · 2024Article
- An Epidemiological Analysis for Assessing and Evaluating COVID-19 Based on Data Analytics in Latin American Countries.Biology · 2023Article
- A Hybridized Machine Learning Approach for Predicting COVID-19 Using Adaptive Neuro-Fuzzy Inference System and Reptile Search Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- Deep learning framework for epidemiological forecasting: A study on COVID-19 cases and deaths in the Amazon state of Pará, Brazil.PloS one · 2023Article
- Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting.PloS one · 2023Article
- AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions.Healthcare (Basel, Switzerland) · 2022Review
- Assessment of COVID-19 impact on sustainable development goals indicators in Egypt using fuzzy analytic hierarchy process.International journal of disaster risk reduction : IJDRR · 2022Article
- Deep learning for Covid-19 forecasting: State-of-the-art review.Neurocomputing · 2022Article
- COVID-19 Vaccination Trends and Side Effects among Egyptian Hemodialysis Patients: A Multicenter Survey Study.Vaccines · 2022Article
- Weather Conditions and COVID-19 Cases: Insights from the GCC Countries.Intelligent systems with applications · 2022Article
- Modeling COVID-19 effects on SDGs using system dynamics in Egypt.Environmental science and pollution research international · 2022Article
- Safety, environmental and risk management related to Covid-19.Process safety and environmental protection : transactions of the Institution of Chemical Engineers, Part B · 2022Article
- Deep Learning-Based Mental Health Model on Primary and Secondary School Students' Quality Cultivation.Computational intelligence and neuroscience · 2022Article
- Pandemic strategies with computational and structural biology against COVID-19: A retrospective.Computational and structural biotechnology journal · 2022Review
- Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics.Network modeling and analysis in health informatics and bioinformatics · 2022Review
- A novel deep interval type-2 fuzzy LSTM (DIT2FLSTM) model applied to COVID-19 pandemic time-series prediction.Journal of biomedical informatics · 2021Article
- Influence of COVID‑19 on the Prognosis and Medication Compliance of Glaucoma Patients in the Nile Delta Region.Clinical ophthalmology (Auckland, N.Z.) · 2021Article
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4 authors.
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Abstract
The World Health Organization has declared COVID-19 as a global pandemic in early 2020. A comprehensive understanding of the epidemiological characteristics of this virus is crucial to limit its spreading. Therefore, this research applies artificial intelligence-based models to predict the prevalence of the COVID-19 outbreak in Egypt. These models are long short-term memory network (LSTM), convolutional neural network, and multilayer perceptron neural network. They are trained and validated using the dataset records from 14 February 2020 to 15 August 2020. The results of the models are evaluated using the determination coefficient and root mean square error. The LSTM model exhibits the best performance in forecasting the cumulative infections for one week and one month ahead. Finally, the LSTM model with the optimal parameter values is applied to forecast the spread of this epidemic for one month ahead using the data from 14 February 2020 to 30 June 2021. The total size of infections, recoveries, and deaths is estimated to be 285,939, 234,747, and 17,251 cases on 31 July 2021. This study could assist the decision-makers in developing and monitoring policies to confront this disease.
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