ArticlePloS one2020
Prediction and analysis of Corona Virus Disease 2019.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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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
18 citing papers in PubMed.
- Biomathematical model to analyze the transmission dynamics of Covid-19: Case study, Santiago de Cali, Colombia.PloS one · 2024Article
- Data-Driven Deep Learning Neural Networks for Predicting the Number of Individuals Infected by COVID-19 Omicron Variant.Epidemiologia (Basel, Switzerland) · 2023Article
- Application of Machine Learning to Predict COVID-19 Spread via an Optimized BPSO Model.Biomimetics (Basel, Switzerland) · 2023Article
- Deep neural network for monitoring the growth of COVID-19 epidemic using meteorological covariates.Intelligent systems with applications · 2023Article
- Improvement of Time Forecasting Models Using Machine Learning for Future Pandemic Applications Based on COVID-19 Data 2020-2022.Diagnostics (Basel, Switzerland) · 2023Article
- Predicting the distribution of COVID-19 through CGAN-Taking Macau as an example.Frontiers in big data · 2023Article
- Predicting COVID-19 using lioness optimization algorithm and graph convolution network.Soft computing · 2023Article
- Analysis of computational intelligence approaches for predicting disease severity in humans: Challenges and research guidelines.Journal of education and health promotion · 2023Review
- Developing forecasting model for future pandemic applications based on COVID-19 data 2020-2022.PloS one · 2023Article
- Forecasting adversities of COVID-19 waves in India using intelligent computing.Innovations in systems and software engineering · 2022Article
- Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data.Journal of healthcare engineering · 2022Article
- Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends.Multimedia systems · 2022Article
- Deep-Data-Driven Neural Networks for COVID-19 Vaccine Efficacy.Epidemiologia (Basel, Switzerland) · 2021Article
- Particle swarm optimization of partitions and fuzzy order for fuzzy time series forecasting of COVID-19.Applied soft computing · 2021Article
- Short-term forecasting of the COVID-19 outbreak in India.International health · 2021Article
- The prediction of the lifetime of the new coronavirus in the USA using mathematical models.Soft computing · 2021Article
- Epidemic Dynamics via Wavelet Theory and Machine Learning with Applications to Covid-19.Biology · 2020Article
- Predictive modeling of COVID-19 death cases in Pakistan.Infectious Disease Modelling · 2020Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The outbreak of Corona Virus Disease 2019 (COVID-19) in Wuhan has significantly impacted the economy and society globally. Countries are in a strict state of prevention and control of this pandemic. In this study, the development trend analysis of the cumulative confirmed cases, cumulative deaths, and cumulative cured cases was conducted based on data from Wuhan, Hubei Province, China from January 23, 2020 to April 6, 2020 using an Elman neural network, long short-term memory (LSTM), and support vector machine (SVM). A SVM with fuzzy granulation was used to predict the growth range of confirmed new cases, new deaths, and new cured cases. The experimental results showed that the Elman neural network and SVM used in this study can predict the development trend of cumulative confirmed cases, deaths, and cured cases, whereas LSTM is more suitable for the prediction of the cumulative confirmed cases. The SVM with fuzzy granulation can successfully predict the growth range of confirmed new cases and new cured cases, although the average predicted values are slightly large. Currently, the United States is the epicenter of the COVID-19 pandemic. We also used data modeling from the United States to further verify the validity of the proposed models.
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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.