SynthesisInternational journal of environmental research and public health2021
Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review.
Synthesis in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis 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
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- A Systematic Review of Artificial Intelligence Applications Used for Inherited Retinal Disease Management.Medicina (Kaunas, Lithuania) · 2022Pooled it
- Phase-Based Motor Skill Acquisition in Preschool Children with Different Participation Experience in a Kinesiology Program.Journal of functional morphology and kinesiology · 2026Article
- Prediction of Cerebrospinal Fluid (CSF) Pressure with Generative Adversarial Network Synthetic Plasma-CSF Biomarker Pairing.Neuroinformatics · 2025Article
- An overview of reviews on digital health interventions during COVID- 19 era: insights and lessons for future pandemics.Archives of public health = Archives belges de sante publique · 2025Article
- Detecting outbreaks using a spatial latent field.PloS one · 2025Article
- AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods.Osong public health and research perspectives · 2024Article
- Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review.JMIR AI · 2023Article
- Article
- On Approximating theBiomedicines · 2023Article
- RETRACTED ARTICLE: Covid-19 classification using sigmoid based hyper-parameter modified DNN for CT scans and chest X-rays.Multimedia tools and applications · 2023Article
- Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting.PloS one · 2023Article
- Prescriptive Analytics-Based SIRM Model for Predicting Covid-19 Outbreak.Global journal of flexible systems management · 2023Article
- Assessment of dietary habits and use of nutritional supplements in COVID-19: A cross-sectional study.PharmaNutrition · 2022Article
- Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews.International journal of medical informatics · 2022Review
- VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants.Computer methods and programs in biomedicine · 2022Article
- Toward smart diagnosis of pandemic infectious diseases using wastewater-based epidemiology.Trends in analytical chemistry : TRAC · 2022Review
- Physical education movement and comprehensive health quality intervention under the background of artificial intelligence.Frontiers in public health · 2022Article
- Advanced Computing Approach for Modeling and Prediction COVID-19 Pandemic.Applied bionics and biomechanics · 2022Article
- SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD.Computers in biology and medicine · 2021Article
- A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries.Life (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 is one of the greatest challenges humanity has faced recently, forcing a change in the daily lives of billions of people worldwide. Therefore, many efforts have been made by researchers across the globe in the attempt of determining the models of COVID-19 spread. The objectives of this review are to analyze some of the open-access datasets mostly used in research in the field of COVID-19 regression modeling as well as present current literature based on Artificial Intelligence (AI) methods for regression tasks, like disease spread. Moreover, we discuss the applicability of Machine Learning (ML) and Evolutionary Computing (EC) methods that have focused on regressing epidemiology curves of COVID-19, and provide an overview of the usefulness of existing models in specific areas. An electronic literature search of the various databases was conducted to develop a comprehensive review of the latest AI-based approaches for modeling the spread of COVID-19. Finally, a conclusion is drawn from the observation of reviewed papers that AI-based algorithms have a clear application in COVID-19 epidemiological spread modeling and may be a crucial tool in the combat against coming pandemics.
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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.