ArticleJournal of healthcare informatics research2021
ALeRT-COVID: Attentive Lockdown-awaRe Transfer Learning for Predicting COVID-19 Pandemics in Different Countries.
Article in Journal of healthcare informatics research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 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
- Smart epidemic control: A hybrid model blending ODEs and agent-based simulations for optimal, real-world intervention planning.PLoS computational biology · 2025Article
- Techniques for learning and transferring knowledge for microbiome-based classification and prediction: review and assessment.Briefings in bioinformatics · 2024Review
- 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
- TransCode: Uncovering COVID-19 transmission patterns via deep learning.Infectious diseases of poverty · 2023Article
- Defining factors in hospital admissions during COVID-19 using LSTM-FCA explainable model.Artificial intelligence in medicine · 2022Article
- Transfer learning for non-image data in clinical research: A scoping review.PLOS digital health · 2022Article
- Artificial intelligence for COVID-19: battling the pandemic with computational intelligence.Intelligent medicine · 2022Review
- Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory.International journal of environmental research and public health · 2021Article
- One-year Review of COVID-19 in the Arab World.Qatar medical journal · 2021Review
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Authors and funding
8 authors.
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Abstract
Countries across the world are in different stages of COVID-19 trajectory, among which many have implemented lockdown measures to prevent its spread. Although the lockdown is effective in such prevention, it may put the economy into a depression. Predicting the epidemic progression with the government switching the lockdown on or off is critical. We propose a transfer learning approach called ALeRT-COVID using attention-based recurrent neural network (RNN) architecture to predict the epidemic trends for different countries. A source model was trained on the pre-defined source countries and then transferred to each target country. The lockdown measure was introduced to our model as a predictor and the attention mechanism was utilized to learn the different contributions of the confirmed cases in the past days to the future trend. Results demonstrated that the transfer learning strategy is helpful especially for early-stage countries. By introducing the lockdown predictor and the attention mechanism, ALeRT-COVID showed a significant improvement in the prediction performance. We predicted the confirmed cases in 1 week when extending and easing lockdown separately. Our results show that lockdown measures are still necessary for several countries. We expect our research can help different countries to make better decisions on the lockdown measures.
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