ReviewIEEE reviews in biomedical engineering2022
A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis.
Review in IEEE reviews in biomedical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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.
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, 1 synthesis or guideline pooled it.
- Applications of Technological Solutions in Primary Ways of Preventing Transmission of Respiratory Infectious Diseases-A Systematic Literature Review.International journal of environmental research and public health · 2021Pooled it
- Machine learning models for early mortality prediction in severe fever with thrombocytopenia syndrome.iScience · 2026Article
- Higher-order Interaction Matters: Modeling Epidemics via Dynamic Hypergraph Neural Networks.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Combining data assimilation of states and parameters for more precise infectious disease prediction.One health (Amsterdam, Netherlands) · 2025Article
- A novel framework for inferring dynamic infectious disease transmission with graph attention: a COVID-19 case study in Korea.BMC public health · 2025Article
- A comparative study on TB incidence and HIVTB coinfection using machine learning models on WHO global TB dataset.Scientific reports · 2025Article
- An efficient parallel runoff forecasting model for capturing global and local feature information.Scientific reports · 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
- Glucocorticoid therapy for sepsis in the AI era: a survey on current and future approaches.Computational and structural biotechnology journal · 2024Review
- Stability analysis and optimal control of a fractional-order generalized SEIR model for the COVID-19 pandemic.Applied mathematics and computation · 2023Article
- Epi-DNNs: Epidemiological priors informed deep neural networks for modeling COVID-19 dynamics.Computers in biology and medicine · 2023Article
- Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists.Bioengineering (Basel, Switzerland) · 2023Article
- Article
- A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms.Mobile networks and applications : MONET · 2023Article
- An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works.Multimedia systems · 2023Article
- A comprehensive review on variants of SARS-CoVs-2: Challenges, solutions and open issues.Computer communications · 2023Article
- Machine learning and bioinformatics analysis to identify autophagy-related biomarkers in peripheral blood for rheumatoid arthritis.Frontiers in genetics · 2023Article
- Deep learning framework for epidemiological forecasting: A study on COVID-19 cases and deaths in the Amazon state of Pará, Brazil.PloS one · 2023Article
- Disease Recognition in X-ray Images with Doctor Consultation-Inspired Model.Journal of imaging · 2022Article
- Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models.Technological forecasting and social change · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 is a life threatening disease which has a enormous global impact. As the cause of the disease is a novel coronavirus whose gene information is unknown, drugs and vaccines are yet to be found. For the present situation, disease spread analysis and prediction with the help of mathematical and data driven model will be of great help to initiate prevention and control action, namely lockdown and qurantine. There are various mathematical and machine-learning models proposed for analyzing the spread and prediction. Each model has its own limitations and advantages for a particluar scenario. This article reviews the state-of-the art mathematical models for COVID-19, including compartment models, statistical models and machine learning models to provide more insight, so that an appropriate model can be well adopted for the disease spread analysis. Furthermore, accurate diagnose of COVID-19 is another essential process to identify the infected person and control further spreading. As the spreading is fast, there is a need for quick auotomated diagnosis mechanism to handle large population. Deep-learning and machine-learning based diagnostic mechanism will be more appropriate for this purpose. In this aspect, a comprehensive review on the deep learning models for the diagnosis of the disease is also provided in this article.
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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.