ArticleComputers in biology and medicine2021
SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-Based Models for Risk Prediction in MASLD: A Systematic Review.Digestive diseases and sciences · 2026Pooled it
- Incorporating meteorological factors into a SARIMA model for predicting pediatric influenza epidemics.Frontiers in public health · 2026Article
- [Analysis of Coronavirus Disease 2019 Prediction Studies in the Republic of Korea].Jugan geon-gang gwa jilbyeong · 2025Review
- A novel framework for inferring dynamic infectious disease transmission with graph attention: a COVID-19 case study in Korea.BMC public health · 2025Article
- Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.Nature communications · 2025Article
- Harnessing artificial intelligence for enhanced public health surveillance: a narrative review.Frontiers in public health · 2025Review
- Assessing the dynamics and impact of COVID-19 vaccination on disease spread: A data-driven approach.Infectious Disease Modelling · 2024Article
- Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.PloS one · 2024Article
- Exploring the influence of environmental indicators and forecasting influenza incidence using ARIMAX models.Frontiers in public health · 2024Article
- Deep learning infused SIRVD model for COVID-19 prediction: XGBoost-SIRVD-LSTM approach.Frontiers in medicine · 2024Article
- EEG-based epileptic seizure detection using binary dragonfly algorithm and deep neural network.Scientific reports · 2023Article
- Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection.Scientific reports · 2023Article
- STG-Net: A COVID-19 prediction network based on multivariate spatio-temporal information.Biomedical signal processing and control · 2023Article
- MTSS-AAE: Multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images.Expert systems with applications · 2023Article
- COVID-19 forecasting using shifted Gaussian Mixture Model with similarity-based estimation.Expert systems with applications · 2023Article
- COVID-19 and human development: An approach for classification of HDI with deep CNN.Biomedical signal processing and control · 2023Article
- Efficient Windows malware identification and classification scheme for plant protection information systems.Frontiers in plant science · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants.Computer methods and programs in biomedicine · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 is one of the biggest challenges that human beings have faced recently. Many researchers have proposed different prediction methods for establishing a virus transmission model and predicting the trend of COVID-19. Among them, the methods based on artificial intelligence are currently the most interesting and widely used. However, only using artificial intelligence methods for prediction cannot capture the time change pattern of the transmission of infectious diseases. To solve this problem, this paper proposes a COVID-19 prediction model based on time-dependent SIRVD by using deep learning. This model combines deep learning technology with the mathematical model of infectious diseases, and forecasts the parameters in the mathematical model of infectious diseases by fusing deep learning models such as LSTM and other time prediction methods. In the current situation of mass vaccination, we analyzed COVID-19 data from January 15, 2021, to May 27, 2021 in seven countries - India, Argentina, Brazil, South Korea, Russia, the United Kingdom, France, Germany, and Italy. The experimental results show that the prediction model not only has a 50% improvement in single-day predictions compared to pure deep learning methods, but also can be adapted to short- and medium-term predictions, which makes the overall prediction more interpretable and robust.
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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.