ArticleEngineering applications of artificial intelligence2023
Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach.
Article in Engineering applications of artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Temporal epidemiology and multi-source forecasting of hemorrhagic fever with renal syndrome and leptospirosis in mainland China: An interpretable machine learning study.Preventive medicine reports · 2026Article
- Epidemiological characteristics and LSTM-based simulation of mumps incidence in Ningbo, China from 2005 to 2024.BMC public health · 2026Article
- Survey on mathematical modeling of infectious disease dynamics: insights and applications.BMC infectious diseases · 2026Review
- A semi-mechanistic modeling strategy for infectious diseases forecasting: Error correction and probabilistic prediction.Biosafety and health · 2026Article
- Article
- Predicting physical activity energy expenditure, types, and intensity using long short-term memory networks in aged 6-18 cross-stage populations.Frontiers in physiology · 2026Article
- [Trends in burden of pelvic fractures from 1990 to 2023 and long short-term memory-based insights into future projections].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2025Article
- Using a multi-strain infectious disease model with physical information neural networks to study the time dependence of SARS-CoV-2 variants of concern.PLoS computational biology · 2025Article
- Prediction analysis of human brucellosis cases in Ili Kazakh Autonomous Prefecture Xinjiang China based on time series.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
- Article
- Dynamic constitutive identification of concrete based on improved dung beetle algorithm to optimize long short-term memory model.Scientific reports · 2024Article
- Article
- Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic.PLoS computational biology · 2023Article
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5 authors.
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Abstract
Individuals in any country are badly impacted both economically and physically whenever an epidemic of infectious illnesses breaks out. A novel coronavirus strain was responsible for the outbreak of the coronavirus sickness in 2019. Corona Virus Disease 2019 (COVID-19) is the name that the World Health Organization (WHO) officially gave to the pneumonia that was caused by the novel coronavirus on February 11, 2020. The use of models that are informed by machine learning is currently a major focus of study in the field of improved forecasting. By displaying annual trends, forecasting models can be of use in performing impact assessments of potential outcomes. In this paper, proposed forecast models consisting of time series models such as long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), generalized regression unit (GRU), and dense-LSTM have been evaluated for time series prediction of confirmed cases, deaths, and recoveries in 12 major countries that have been affected by COVID-19. Tensorflow1.0 was used for programming. Indices known as mean absolute error (MAE), root means square error (RMSE), Median Absolute Error (MEDAE) and r2 score are utilized in the process of evaluating the performance of models. We presented various ways to time-series forecasting by making use of LSTM models (LSTM, BiLSTM), and we compared these proposed methods to other machine learning models to evaluate the performance of the models. Our study suggests that LSTM based models are among the most advanced models to forecast time series data.
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