ArticleNeurocomputing2022
Time series predicting of COVID-19 based on deep learning.
Article in Neurocomputing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Personalised Blood Glucose Time Series Forecasting in Type 1 Diabetes: Deep Collaborative Adversarial Learning.Journal of personalized medicine · 2026Article
- Computational proteomics to enhance personalized treatment of COVID-19 and Long COVID.Clinical proteomics · 2026Review
- A semi-mechanistic modeling strategy for infectious diseases forecasting: Error correction and probabilistic prediction.Biosafety and health · 2026Article
- Short-term forecasting and impact analysis of COVID-19 and the stock market in Morocco using ARIMA.PloS one · 2026Article
- Predictive and interpretable machine learning for COVID-19 resurgences: the role of SARS-CoV-2 variants in the post-pandemic era.BMC infectious diseases · 2025Article
- Combining data assimilation of states and parameters for more precise infectious disease prediction.One health (Amsterdam, Netherlands) · 2025Article
- Prediction of COVID-19 cases by multifactor driven long short-term memory (LSTM) model.Scientific reports · 2025Article
- Infectious disease prediction model based on optimized deep learning algorithm.Frontiers in public health · 2025Article
- Machine learning in knee injury sequelae detection: Unravelling the role of psychological factors and preventing long-term sequelae.Journal of experimental orthopaedics · 2024Article
- Artificial Intelligence-Driven Prognosis of Respiratory Mechanics: Forecasting Tissue Hysteresivity Using Long Short-Term Memory and Continuous Sensor Data.Sensors (Basel, Switzerland) · 2024Article
- Deep learning infused SIRVD model for COVID-19 prediction: XGBoost-SIRVD-LSTM approach.Frontiers in medicine · 2024Article
- Article
- Deep learning based hybrid prediction model for predicting the spread of COVID-19 in the world's most populous countries.Expert systems with applications · 2023Article
- A new time-varying coefficient regression approach for analyzing infectious disease data.Scientific reports · 2023Article
- Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China.PLoS neglected tropical diseases · 2023Article
- Article
- Analysis and Prediction of COVID-19 Multivariate Data Using Deep Ensemble Learning Methods.International journal of environmental research and public health · 2023Article
- Multi-weight susceptible-infected model for predicting COVID-19 in China.Neurocomputing · 2023Article
- Estimate the incubation period of coronavirus 2019 (COVID-19).Computers in biology and medicine · 2023Article
- An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 was declared a global pandemic by the World Health Organisation (WHO) on 11th March 2020. Many researchers have, in the past, attempted to predict a COVID outbreak and its effect. Some have regarded time-series variables as primary factors which can affect the onset of infectious diseases like influenza and severe acute respiratory syndrome (SARS). In this study, we have used public datasets provided by the European Centre for Disease Prevention and Control for developing a prediction model for the spread of the COVID-19 outbreak to and throughout Malaysia, Morocco and Saudi Arabia. We have made use of certain effective deep learning (DL) models for this purpose. We assessed some specific major features for predicting the trend of the existing COVID-19 outbreak in these three countries. In this study, we also proposed a DL approach that includes recurrent neural network (RNN) and long short-term memory (LSTM) networks for predicting the probable numbers of COVID-19 cases. The LSTM models showed a 98.58% precision accuracy while the RNN models showed a 93.45% precision accuracy. Also, this study compared the number of coronavirus cases and the number of resulting deaths in Malaysia, Morocco and Saudi Arabia. Thereafter, we predicted the number of confirmed COVID-19 cases and deaths for a subsequent seven days. In this study, we presented their predictions using the data that was available up to December 3rd, 2020.
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Registered trials
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