ArticleResults in physics2021
Forecasting COVID-19 cases: A comparative analysis between recurrent and convolutional neural networks.
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 28 papers.
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Who cites it
28 citing papers in PubMed, 70 citations in OpenAlex.
- 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
- COVID-19 health data prediction: a critical evaluation of CNN-based approaches.Scientific reports · 2025Article
- Impact and Effectiveness of COVID-19 Vaccines Based on Machine Learning Analysis of a Time Series: A Population-Based Study.Journal of clinical medicine · 2024Article
- Enhancing COVID-19 forecasting precision through the integration of compartmental models, machine learning and variants.Scientific reports · 2024Article
- An analysis of the waning effect of COVID-19 vaccinations.Genomics & informatics · 2023Article
- Article
- Epi-DNNs: Epidemiological priors informed deep neural networks for modeling COVID-19 dynamics.Computers in biology and medicine · 2023Article
- Data Mining Based Techniques for Covid-19 Predictions.Procedia computer science · 2023Article
- Outbreak prediction of COVID-19 using Recurrent neural network with Gated Recurrent Units.Materials today. Proceedings · 2023Article
- DeepCov: Effective Prediction Model of COVID-19 Using CNN Algorithm.SN computer science · 2023Article
- Dynamic transmission modeling of COVID-19 to support decision-making in Brazil: A scoping review in the pre-vaccine era.PLOS global public health · 2023Article
- Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural networks.PloS one · 2023Article
- An interpretable DIC risk prediction model based on convolutional neural networks with time series data.BMC bioinformatics · 2022Article
- Deep learning for Covid-19 forecasting: State-of-the-art review.Neurocomputing · 2022Article
- A case study of 2019-nCoV in Russia using integer and fractional order derivatives.Mathematical methods in the applied sciences · 2022Article
- VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants.Computer methods and programs in biomedicine · 2022Article
- A Novel Approach on Deep Learning-Based Decision Support System Applying Multiple Output LSTM-Autoencoder: Focusing on Identifying Variations by PHSMs' Effect over COVID-19 Pandemic.International journal of environmental research and public health · 2022Article
- Mathematical modeling of the impact of Omicron variant on the COVID-19 situation in South Korea.Genomics & informatics · 2022Article
- Temporal deep learning architecture for prediction of COVID-19 cases in India.Expert systems with applications · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Though many countries have already launched COVID-19 mass vaccination programs to control the disease outbreak quickly, numerous countries around worldwide are grappling with unprecedented surges of new COVID-19 cases due to a more contagious and deadly variant of coronavirus. As the number of new cases is skyrocketing, pandemic fatigue and public apathy towards different intervention strategies pose new challenges to government officials to combat the pandemic. Henceforth, it is indispensable for the government officials to understand the future dynamics of COVID-19 flawlessly to develop strategic preparedness and resilient response planning. In light of the above circumstances, probable future outbreak scenarios in Brazil, Russia, and the United kingdom have been sketched in this study with the help of four deep learning models: long short term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN) and multivariate convolutional neural network (MCNN). In our analysis, the CNN algorithm has outperformed other deep learning models in terms of validation accuracy and forecasting consistency. It is unearthed in our study that CNN can provide robust long-term forecasting results in time-series analysis due to its capability of essential features learning, distortion invariance, and temporal dependence learning. However, the prediction accuracy of the LSTM algorithm has been found to be poor as it tries to discover seasonality and periodic intervals from any time-series dataset, which were absent in our studied countries. Our study has highlighted the promising validation of using convolutional neural networks instead of recurrent neural networks when forecasting with very few features and less amount of historical data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.