ArticleHeliyon2021
Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review.
Article in Heliyon, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 24 papers, 1 of them a synthesis that pooled it.
What it found
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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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data-driven strategies for model-informed decision-making during the COVID-19 pandemic: a systematic review.BMJ open · 2026Pooled it
- Features of the land use and COVID-19 cases and deaths in urban and land counties in Poland throughout the pandemic: a machine learning approach.BMC public health · 2026Article
- Article
- Statistical modelling and forecasting of HIV and anti-retroviral therapy cases by time-series and machine learning models.Scientific reports · 2025Article
- Smart epidemic control: A hybrid model blending ODEs and agent-based simulations for optimal, real-world intervention planning.PLoS computational biology · 2025Article
- Article
- Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach.Scientific reports · 2025Article
- Methodology for predicting hospital admissions and evaluating recovery rates for coronavirus disease in Japan.PloS one · 2025Article
- Discovering Time-Varying Public Interest for COVID-19 Case Prediction in South Korea Using Search Engine Queries: Infodemiology Study.Journal of medical Internet research · 2024Article
- A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction.Scientific reports · 2024Article
- Article
- An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction.Biomimetics (Basel, Switzerland) · 2023Article
- Estimate the incubation period of coronavirus 2019 (COVID-19).Computers in biology and medicine · 2023Article
- Improved healthcare disaster decision-making utilizing information extraction from complementary social media data during the COVID-19 pandemic.Decision support systems · 2023Article
- Investigating the performance of machine learning algorithms in predicting the survival of COVID-19 patients: A cross section study of Iran.Health science reports · 2023Article
- Article
- Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters.Gondwana research : international geoscience journal · 2023Article
- Article
- Article
- Exploring Socioeconomic Status as a Global Determinant of COVID-19 Prevalence, Using Exploratory Data Analytic and Supervised Machine Learning Techniques: Algorithm Development and Validation Study.JMIR formative research · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 has produced a global pandemic affecting all over of the world. Prediction of the rate of COVID-19 spread and modeling of its course have critical impact on both health system and policy makers. Indeed, policy making depends on judgments formed by the prediction models to propose new strategies and to measure the efficiency of the imposed policies. Based on the nonlinear and complex nature of this disorder and difficulties in estimation of virus transmission features using traditional epidemic models, artificial intelligence methods have been applied for prediction of its spread. Based on the importance of machine and deep learning approaches in the estimation of COVID-19 spreading trend, in the present study, we review studies which used these strategies to predict the number of new cases of COVID-19. Adaptive neuro-fuzzy inference system, long short-term memory, recurrent neural network and multilayer perceptron are among the mostly used strategies in this regard. We compared the performance of several machine learning methods in prediction of COVID-19 spread. Root means squared error (RMSE), mean absolute error (MAE), R
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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.