ArticleResults in physics2021
Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods.
Article in Results in physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Spatio-temporal epidemic forecasting with graph-based transformer.International journal of health geographics · 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
- Evolution of artificial intelligence in healthcare: a 30-year bibliometric study.Frontiers in medicine · 2024Article
- Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression.Cognitive neurodynamics · 2023Article
- Prediction of outpatient waiting time: using machine learning in a tertiary children's hospital.Translational pediatrics · 2023Article
- Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic.World journal of clinical cases · 2023Review
- Transformers for Energy Forecast.Sensors (Basel, Switzerland) · 2023Article
- Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach.Engineering applications of artificial intelligence · 2023Article
- Body composition predicts hypertension using machine learning methods: a cohort study.Scientific reports · 2023Article
- COVID-19 forecasting using shifted Gaussian Mixture Model with similarity-based estimation.Expert systems with applications · 2023Article
- COVID-19 spread control policies based early dynamics forecasting using deep learning algorithm.Chaos, solitons, and fractals · 2023Article
- A residual network-based framework for COVID-19 detection from CXR images.Neural computing & applications · 2023Article
- Using machine learning prediction models for quality control: a case study from the automotive industry.Computational management science · 2023Article
- DeepCov: Effective Prediction Model of COVID-19 Using CNN Algorithm.SN computer science · 2023Article
- Time series forecasting of COVID-19 infections and deaths in Alpha and Delta variants using LSTM networks.PloS one · 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
- Breast Cancer Dataset, Classification and Detection Using Deep Learning.Healthcare (Basel, Switzerland) · 2022Review
- The negative impact of the COVID-19 on renewable energy growth in developing countries: Underestimated.Journal of cleaner production · 2022Article
- LiteCovidNet: A lightweight deep neural network model for detection of COVID-19 using X-ray images.International journal of imaging systems and technology · 2022Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.
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