ArticleArray (New York, N.Y.)2021
Prediction of the number of COVID-19 confirmed cases based on K-means-LSTM.
Article in Array (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses 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.
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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, 2 syntheses or guidelines pooled it.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Methodology for predicting hospital admissions and evaluating recovery rates for coronavirus disease in Japan.PloS one · 2025Article
- Comprehensive Clustering Analysis and Profiling of COVID-19 Vaccine Hesitancy and Related Factors across U.S. Counties: Insights for Future Pandemic Responses.Healthcare (Basel, Switzerland) · 2024Article
- Multi-Sensor Platform for Predictive Air Quality Monitoring.Sensors (Basel, Switzerland) · 2023Article
- An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM.Scientific reports · 2023Article
- Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases.Frontiers in artificial intelligence · 2023Article
- Possibility of the COVID-19 third wave in India: mapping from second wave to third wave.Indian journal of physics and proceedings of the Indian Association for the Cultivation of Science (2004) · 2023Article
- Deep learning for Covid-19 forecasting: State-of-the-art review.Neurocomputing · 2022Article
- Weather Conditions and COVID-19 Cases: Insights from the GCC Countries.Intelligent systems with applications · 2022Article
- An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling.Annals of data science · 2022Article
- Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review.Artificial intelligence in medicine · 2022Review
- Combining rank-size and k-means for clustering countries over the COVID-19 new deaths per million.Chaos, solitons, and fractals · 2022Article
- Influence of state reopening policies in COVID-19 mortality.Scientific reports · 2022Article
- Role of Hypertension on the Severity of COVID-19: A Review.Journal of cardiovascular pharmacology · 2021Review
- Applications of artificial intelligence in battling against covid-19: A literature review.Chaos, solitons, and fractals · 2021Review
- Epidemiological Predictive Modeling of COVID-19 Infection: Development, Testing, and Implementation on the Population of the Benelux Union.Frontiers in public health · 2021Article
- Physical and pulmonary capacities of individuals with severe coronavirus disease after hospital discharge: A preliminary cross-sectional study based on cluster analysis.Clinics (Sao Paulo, Brazil)Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 is a pandemic disease that began to rapidly spread in the US, with the first case detected on January 19, 2020, in Washington State. March 9, 2020, and then quickly increased with total cases of 25,739 as of April 20, 2020. Although most people with coronavirus 81%, according to the U.S. Centers for Disease Control and Prevention (CDC), will have little to mild symptoms, others may rely on a ventilator to breathe or not at all. SEIR models have broad applicability in predicting the outcome of the population with a variety of diseases. However, many researchers use these models without validating the necessary hypotheses. Far too many researchers often "overfit" the data by using too many predictor variables and small sample sizes to create models. Models thus developed are unlikely to stand validity check on a separate group of population and regions. The researcher remains unaware that overfitting has occurred, without attempting such validation. In the paper, we present a combination algorithm that combines similar days features selection based on the region using Xgboost, K-Means, and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., K-Means-LSTM) for short-term COVID-19 cases forecasting in Louisana state USA. The weighted k-means algorithm based on extreme gradient boosting is used to evaluate the similarity between the forecasts and past days. The results show that the method with K-Means-LSTM has a higher accuracy with an RMSE of 601.20 whereas the SEIR model with an RMSE of 3615.83.
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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.