ReviewNeural computing & applications2021
A review on COVID-19 forecasting models.
Review in Neural computing & applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 2 of them syntheses that pooled it.
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
93 citing papers in PubMed, 2 syntheses or guidelines pooled it, 248 citations in OpenAlex.
- Trends and developments in health systems modeling: a bibliometric analysis.Frontiers in digital health · 2025Pooled it
- A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic.Frontiers in genetics · 2022Pooled it
- Pathogens on High-Touch Surfaces in an Arid Megacity: A Longitudinal Molecular Surveillance Study.Microorganisms · 2026Article
- Understanding pandemics through molecular gas dynamics.PNAS nexus · 2025Article
- Spread of the plague in Venice, 1630-1631: epidemic entropy in a "natural experiment".medRxiv : the preprint server for health sciences · 2025Article
- Modeling the Complete Dynamics of the SARS-CoV-2 Pandemic of Germany and Its Federal States Using Multiple Levels of Data.Viruses · 2025Article
- A Forecast Model for COVID-19 Spread Trends Using Blog and GPS Data from Smartphones.Entropy (Basel, Switzerland) · 2025Article
- An ensemble approach improves the prediction of the COVID-19 pandemic in South Korea.Journal of global health · 2025Article
- Impact of COVID-19 vaccinations in India: a state-wise analysis.BMC public health · 2025Article
- Baseline predictors for 28-day COVID-19 severity and mortality among hospitalized patients: results from the IMPACC study.Frontiers in medicine · 2025Article
- Article
- Mathematical assessment of the roles of age heterogeneity and vaccination on the dynamics and control of SARS-CoV-2.Infectious Disease Modelling · 2024Article
- Enhancing COVID-19 forecasting precision through the integration of compartmental models, machine learning and variants.Scientific reports · 2024Article
- Dynamic hierarchical state space forecasting.Statistics in medicine · 2024Article
- Agent-based modeling to estimate the impact of lockdown scenarios and events on a pandemic exemplified on SARS-CoV-2.Scientific reports · 2024Article
- Article
- Real-time forecasting of COVID-19-related hospital strain in France using a non-Markovian mechanistic model.PLoS computational biology · 2024Article
- A data-driven combined prediction method for the demand for intensive care unit healthcare resources in public health emergencies.BMC health services research · 2024Article
- AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods.Osong public health and research perspectives · 2024Article
- Estimating actual SARS-CoV-2 infections from secondary data.Scientific reports · 2024Article
33 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The novel coronavirus (COVID-19) has spread to more than 200 countries worldwide, leading to more than 36 million confirmed cases as of October 10, 2020. As such, several machine learning models that can forecast the outbreak globally have been released. This work presents a review and brief analysis of the most important machine learning forecasting models against COVID-19. The work presented in this study possesses two parts. In the first section, a detailed scientometric analysis presents an influential tool for bibliometric analyses, which were performed on COVID-19 data from the Scopus and Web of Science databases. For the above-mentioned analysis, keywords and subject areas are addressed, while the classification of machine learning forecasting models, criteria evaluation, and comparison of solution approaches are discussed in the second section of the work. The conclusion and discussion are provided as the final sections of this study.
Indexed as
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.