ArticleBMC bioinformatics2021
Forecasting the spread of COVID-19 using LSTM network.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.Computer methods and programs in biomedicine · 2022Pooled it
- Combination forecasting of COVID-19 in Bangladesh: guiding public health policy through integrated time series, ML, and DL models.BMC infectious diseases · 2025Article
- Bioinformatics and molecular biology tools for diagnosis, prevention, treatment and prognosis of COVID-19.Heliyon · 2024Review
- Article
- Innovative applications of artificial intelligence during the COVID-19 pandemic.Infectious medicine · 2024Review
- Prediction of cross-border spread of the COVID-19 pandemic: A predictive model for imported cases outside China.PloS one · 2024Article
- Forecasting of Covid-19 positive cases in Indonesia using long short-term memory (LSTM).Procedia computer science · 2023Article
- BERT-5mC: an interpretable model for predicting 5-methylcytosine sites of DNA based on BERT.PeerJ · 2023Article
- Deep learning for Covid-19 forecasting: State-of-the-art review.Neurocomputing · 2022Article
- Defining factors in hospital admissions during COVID-19 using LSTM-FCA explainable model.Artificial intelligence in medicine · 2022Article
- Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model.BMC medicine · 2022Article
- Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning.Informatics in medicine unlocked · 2021Article
- A COVID-19 forecasting system for hospital needs using ANFIS and LSTM models: A graphical user interface unit.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
Abstract
backgroundThe novel coronavirus (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2, and within a few months, it has become a global pandemic. This forced many affected countries to take stringent measures such as complete lockdown, shutting down businesses and trade, as well as travel restrictions, which has had a tremendous economic impact. Therefore, having knowledge and foresight about how a country might be able to contain the spread of COVID-19 will be of paramount importance to the government, policy makers, business partners and entrepreneurs. To help social and administrative decision making, a model that will be able to forecast when a country might be able to contain the spread of COVID-19 is needed.
resultsThe results obtained using our long short-term memory (LSTM) network-based model are promising as we validate our prediction model using New Zealand's data since they have been able to contain the spread of COVID-19 and bring the daily new cases tally to zero. Our proposed forecasting model was able to correctly predict the dates within which New Zealand was able to contain the spread of COVID-19. Similarly, the proposed model has been used to forecast the dates when other countries would be able to contain the spread of COVID-19.
conclusionThe forecasted dates are only a prediction based on the existing situation. However, these forecasted dates can be used to guide actions and make informed decisions that will be practically beneficial in influencing the real future. The current forecasting trend shows that more stringent actions/restrictions need to be implemented for most of the countries as the forecasting model shows they will take over three months before they can possibly contain the spread 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.