ArticleISA transactions2022
Transfer Learning for COVID-19 cases and deaths forecast using LSTM network.
Article in ISA transactions, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions.Computers in biology and medicine · 2022Pooled it
- Selecting synthetic data for successful simulation-based transfer learning in dynamical biological systems.BMC bioinformatics · 2026Article
- Spatiotemporal dynamics, clustering, and ARIMA-based short-term prediction of human brucellosis in Xinjiang, China.BMC infectious diseases · 2026Article
- Global approaches to infectious disease surveillance and modeling.Nature medicine · 2026Review
- Multiple change point analysis and time series forecast of brucellosis reports in Xinjiang, China from 2010 to 2024.BMC public health · 2025Article
- Similarity based city data transfer framework in urban digitization.Scientific reports · 2025Article
- A data-driven combined prediction method for the demand for intensive care unit healthcare resources in public health emergencies.BMC health services research · 2024Article
- Optimizing extubation success: a comparative analysis of time series algorithms and activation functions.Frontiers in computational neuroscience · 2024Article
- Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic.PLoS computational biology · 2023Article
- Prediction of COVID-19 Using a WOA-BILSTM Model.Bioengineering (Basel, Switzerland) · 2023Article
- Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning.Scientific reports · 2023Article
- Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach.Engineering applications of artificial intelligence · 2023Article
- Transfer-recursive-ensemble learning for multi-day COVID-19 prediction in India using recurrent neural networks.Scientific reports · 2023Article
- A novel prediction model based on decomposition-integration and error correction for COVID-19 daily confirmed and death cases.Computers in biology and medicine · 2023Article
- COVID-19 forecasting using shifted Gaussian Mixture Model with similarity-based estimation.Expert systems with applications · 2023Article
- TransCode: Uncovering COVID-19 transmission patterns via deep learning.Infectious diseases of poverty · 2023Article
- Forecasting COVID-19 and Other Infectious Diseases for Proactive Policy: Artificial Intelligence Can Help.Journal of urban health : bulletin of the New York Academy of Medicine · 2023Article
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
- 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
- Predicting mild cognitive impairment among Chinese older adults: a longitudinal study based on long short-term memory networks and machine learning.Frontiers in aging neuroscience · 2023Article
Corrections and comments
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1 author.
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Abstract
In this paper, Transfer Learning is used in LSTM networks to forecast new COVID cases and deaths. Models trained in data from early COVID infected countries like Italy and the United States are used to forecast the spread in other countries. Single and multistep forecasting is performed from these models. The results from these models are tested with data from Germany, France, Brazil, India, and Nepal to check the validity of the method. The obtained forecasts are promising and can be helpful for policymakers coping with the threats of COVID-19.
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Registered trials
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.