ArticleScientific reports2021
The prediction and analysis of COVID-19 epidemic trend by combining LSTM and Markov method.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
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- Fine-grained forecasting of COVID-19 trends at the county level in the United States.NPJ digital medicine · 2025Article
- A Markov network approach for reproducing purchase behaviours observed in convenience stores.Scientific reports · 2024Article
- Enhanced SARS-CoV-2 case prediction using public health data and machine learning models.JAMIA open · 2024Article
- Interrelationships between urban travel demand and electricity consumption: a deep learning approach.Scientific reports · 2023Article
- The dynamics of novel corona virus disease via stochastic epidemiological model with vaccination.Scientific reports · 2023Article
- Seasonal Variation Analysis for Weekly Cases, Deaths, and Hospitalizations of COVID-19 in the United States.Advances in experimental medicine and biology · 2023Article
- Study on Volleyball-Movement Pose Recognition Based on Joint Point Sequence.Computational intelligence and neuroscience · 2023Article
- Research on named entity recognition method of marine natural products based on attention mechanism.Frontiers in chemistry · 2023Article
- Deep learning for Covid-19 forecasting: State-of-the-art review.Neurocomputing · 2022Article
- A Novel Sensor System for In Vivo Perception Reconstruction Based on Long Short-Term Memory Networks.Sensors (Basel, Switzerland) · 2022Article
- Correlated stochastic epidemic model for the dynamics of SARS-CoV-2 with vaccination.Scientific reports · 2022Article
- Development of neural network models for prediction of the outcome of COVID-19 hospitalized patients based on initial laboratory findings, demographics, and comorbidities.Journal of family medicine and primary care · 2022Article
- Forecasting COVID-19 Epidemic Trends by Combining a Neural Network withEntropy (Basel, Switzerland) · 2022Article
- Development and comparison of predictive models for sexually transmitted diseases-AIDS, gonorrhea, and syphilis in China, 2011-2021.Frontiers in public health · 2022Article
- Human Sports Action and Ideological and PoliticalEvaluation by Lightweight Deep Learning Model.Computational intelligence and neuroscience · 2022Article
- Article
- 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
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5 authors.
Funding
Abstract
Corona Virus Disease 2019 (COVID-19) has spread rapidly to countries all around the world from the end of 2019, which caused a great impact on global health and has had a huge impact on many countries. Since there is still no effective treatment, it is essential to making effective predictions for relevant departments to make responses and arrangements in advance. Under the limited data, the prediction error of LSTM model will increase over time, and its prone to big bias for medium- and long-term prediction. To overcome this problem, our study proposed a LSTM-Markov model, which uses Markov model to reduce the prediction error of LSTM model. Based on confirmed case data in the US, Britain, Brazil and Russia, we calculated the training errors of LSTM and constructed the probability transfer matrix of the Markov model by the errors. And finally, the prediction results were obtained by combining the output data of LSTM model with the prediction errors of Markov Model. The results show that: compared with the prediction results of the classical LSTM model, the average prediction error of LSTM-Markov is reduced by more than 75%, and the RMSE is reduced by more than 60%, the mean [Formula: see text] of LSTM-Markov is over 0.96. All those indicators demonstrate that the prediction accuracy of proposed LSTM-Markov model is higher than that of the LSTM model to reach more accurate prediction of COVID-19.
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