ArticleEnvironmental science and pollution research international2021
Predicting COVID-19 cases using bidirectional LSTM on multivariate time series.
Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 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
- Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review.Bioengineering (Basel, Switzerland) · 2025Review
- Predictive modeling of biomedical temporal data in healthcare applications: review and future directions.Frontiers in physiology · 2024Review
- Cluster analysis and forecasting of viruses incidence growth curves: Application to SARS-CoV-2.Expert systems with applications · 2023Article
- Projection of morbidity and mortality due to breast cancer between 2020 and 2050 across 42 low- and middle-income countries.Heliyon · 2023Article
- Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters.Gondwana research : international geoscience journal · 2023Article
- COVID-19 spread control policies based early dynamics forecasting using deep learning algorithm.Chaos, solitons, and fractals · 2023Article
- Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models.Technological forecasting and social change · 2022Article
- Temporal deep learning architecture for prediction of COVID-19 cases in India.Expert systems with applications · 2022Article
- Predicting COVID-19 Cases From Atmospheric Parameters Using Machine Learning Approach.GeoHealth · 2022Article
- Adaptively temporal graph convolution model for epidemic prediction of multiple age groups.Fundamental research · 2022Article
- A deep learning framework using CNN and stacked Bi-GRU for COVID-19 predictions in India.Signal, image and video processing · 2022Article
- Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network.Scientific reports · 2021Article
- Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review.Heliyon · 2021Article
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4 authors.
Funding
Abstract
To assist policymakers in making adequate decisions to stop the spread of the COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series. Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. We validate the effectiveness of the proposed approach by studying the disease outbreak in Qatar and the proposed model prediction from December 1st until December 31st, 2020. The quantitative evaluation shows that the proposed technique outperforms state-of-art forecasting approaches.
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