ReviewHealthcare (Basel, Switzerland)2021
Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview.
Review in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
16 citing papers in PubMed.
- Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Spatio-temporal analysis of COVID-19 lockdown effect to survive in the US counties using ANN.Scientific reports · 2024Article
- Forecasting the spread of COVID-19 based on policy, vaccination, and Omicron data.Scientific reports · 2024Article
- Mechanisms of Piperacillin/Tazobactam Nephrotoxicity: Piperacillin/Tazobactam-Induced Direct Tubular Damage in Mice.Antibiotics (Basel, Switzerland) · 2023Article
- Data driven contagion risk management in low-income countries using machine learning applications with COVID-19 in South Asia.Scientific reports · 2023Article
- Artificial Intelligence Applied to Improve Scientific Reviews: The Antibacterial Activity ofAntibiotics (Basel, Switzerland) · 2023Article
- Evolution of Antimicrobial Susceptibility to Penicillin in Invasive Strains ofAntibiotics (Basel, Switzerland) · 2023Article
- Wide-Scope Target and Suspect Screening of Antibiotics in Effluent Wastewater from Wastewater Treatment Plants in Europe.Antibiotics (Basel, Switzerland) · 2023Article
- Article
- Unveiling the predictive power: a comprehensive study of machine learning model for anticipating chronic kidney disease.Frontiers in artificial intelligence · 2023Article
- Predicting the Temperature Evolution during Nanomilling of Drug Suspensions via a Semi-Theoretical Lumped-Parameter Model.Pharmaceutics · 2022Article
- Forecasting admissions in psychiatric hospitals before and during Covid-19: a retrospective study with routine data.Scientific reports · 2022Article
- Mathematical modeling and analysis of the SARS-Cov-2 disease with reinfection.Computational biology and chemistry · 2022Article
- Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete.Materials (Basel, Switzerland) · 2022Article
- Towards edge devices implementation: deep learning model with visualization for COVID-19 prediction from chest X-ray.Advances in computational intelligence · 2022Article
- Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models.Journal of healthcare engineering · 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
Abstract
Since the discovery of COVID-19 at the end of 2019, a significant surge in forecasting publications has been recorded. Both statistical and artificial intelligence (AI) approaches have been reported; however, the AI approaches showed a better accuracy compared with the statistical approaches. This study presents a review on the applications of different AI approaches used in forecasting the spread of this pandemic. The fundamentals of the commonly used AI approaches in this context are briefly explained. Evaluation of the forecasting accuracy using different statistical measures is introduced. This review may assist researchers, experts and policy makers involved in managing the COVID-19 pandemic to develop more accurate forecasting models and enhanced strategies to control the spread of this pandemic. Additionally, this review study is highly significant as it provides more important information of AI applications in forecasting the prevalence of this pandemic.
Indexed as
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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.