ArticleArchives of computational methods in engineering : state of the art reviews2021
The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges.
Article in Archives of computational methods in engineering : state of the art reviews, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 3 of them syntheses 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Critical Analysis of the Current Medical Image-Based Processing Techniques for Automatic Disease Evaluation: Systematic Literature Review.Sensors (Basel, Switzerland) · 2022Pooled it
- A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic.Frontiers in genetics · 2022Pooled 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
- Diagnostic accuracy of image-based deep learning for glaucomatous optic neuropathy detection: a systematic review and meta-analysis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Global or local modeling for XGBoost in geospatial studies upon simulated data and German COVID-19 infection forecasting.Scientific reports · 2025Article
- New classifications for quantum bioinformatics: Q-bioinformatics, QCt-bioinformatics, QCg-bioinformatics, and QCr-bioinformatics.Briefings in bioinformatics · 2024Review
- Deep learning for Covid-19 forecasting: State-of-the-art review.Neurocomputing · 2022Article
- 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
- A sustainable advanced artificial intelligence-based framework for analysis of COVID-19 spread.Environment, development and sustainability · 2022Article
- Enabling Artificial Intelligence for Genome Sequence Analysis of COVID-19 and Alike Viruses.Interdisciplinary sciences, computational life sciences · 2022Article
- COVID-19 Spread Mapper: a multi-resolution, unified framework and open-source tool.Bioinformatics (Oxford, England) · 2022Article
- Modelling Covid-19 infections in Zambia using data mining techniques.Results in engineering · 2022Article
- Review on COVID-19 diagnosis models based on machine learning and deep learning approaches.Expert systems · 2022Article
- Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook.The Science of the total environment · 2022Review
- Framework for enhancing the estimation of model parameters for data with a high level of uncertainty.Nonlinear dynamics · 2022Article
- Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends.Multimedia systems · 2022Article
- Differential evolution and particle swarm optimization against COVID-19.Artificial intelligence review · 2022Article
- Multi-criteria text mining model for COVID-19 testing reasons and symptoms and temporal predictive model for COVID-19 test results in rural communities.Neural computing & applications · 2022Article
- Statistical analysis of blood characteristics of COVID-19 patients and their survival or death prediction using machine learning algorithms.Neural computing & applications · 2022Article
- Covid19/IT the digital side of Covid19: A picture from Italy with clustering and taxonomy.PloS one · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make predictions about the events. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.
Identifiers
What OpenQuestion holds
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.