ArticleEnvironmental science and pollution research international2021
The COVID-19 pandemic: prediction study based on machine learning models.
Article in Environmental science and pollution research international, 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.
- Model-free prognostication of non-linear time series.PloS one · 2026Article
- Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review.Bioengineering (Basel, Switzerland) · 2025Review
- Using meta-learning to recommend an appropriate time-series forecasting model.BMC public health · 2024Article
- COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Minimizing Viral Transmission in COVID-19 Like Pandemics: Technologies, Challenges, and Opportunities.IEEE sensors journal · 2023Article
- A Hybrid Model for Coronavirus Disease 2019 Forecasting Based on Ensemble Empirical Mode Decomposition and Deep Learning.International journal of environmental research and public health · 2022Article
- A Robust Design-Based Expert System for Feature Selection and COVID-19 Pandemic Prediction in Japan.Healthcare (Basel, Switzerland) · 2022Article
- Weather Conditions and COVID-19 Cases: Insights from the GCC Countries.Intelligent systems with applications · 2022Article
- Estimation of COVID-19 patient numbers using artificial neural networks based on air pollutant concentration levels.Environmental science and pollution research international · 2022Article
- Prediction and forecasting of worldwide corona virus (COVID-19) outbreak using time series and machine learning.Concurrency and computation : practice & experience · 2022Article
- Novel statistics predict the COVID-19 pandemic could terminate in 2022.Journal of medical virology · 2022Article
- Novel deep learning approach to model and predict the spread of COVID-19.Intelligent systems with applications · 2022Article
- System dynamics analysis of COVID-19 prevention and control strategies.Environmental science and pollution research international · 2022Article
- Comparative Study Based on Analysis of Coronavirus Disease (COVID-19) Detection and Prediction Using Machine Learning Models.SN computer science · 2022Review
- Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review.Heliyon · 2021Article
- Environmental factors and the epidemics of COVID-19.Environmental science and pollution research international · 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 was first discovered in Wuhan, China in December 2019. It is one of the worst pandemics in human history. Recent studies reported that COVID-19 is transmitted among humans by droplet infection or direct contact. COVID-19 pandemic has invaded more than 210 countries around the world and as of February 18
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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.