ArticleInternet of things (Amsterdam, Netherlands)2020
Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing.
Article in Internet of things (Amsterdam, Netherlands), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 120 papers, 2 of them syntheses 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.
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Who cites it
120 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Monitoring reported SARS-CoV-2 variants to assess the status of COVID-19 epidemics in the low epidemic state.Scientific reports · 2025Article
- A machine learning approach for diagnostic and prognostic predictions, key risk factors and interactions.Health services & outcomes research methodology · 2025Article
- Spatio-temporal analysis of COVID-19 lockdown effect to survive in the US counties using ANN.Scientific reports · 2024Article
- Health information management systems and practices in conflict-affected settings: the case of northwest Syria.Globalization and health · 2024Article
- Development of the second version of Global Prediction System for Epidemiological Pandemic.Fundamental research · 2024Article
- AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods.Osong public health and research perspectives · 2024Article
- Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects.Heliyon · 2024Review
- Spatiotemporal dynamics of epidemiology diseases: mobility based risk and short-term prediction modeling of COVID-19.Frontiers in public health · 2024Article
- A novel bidirectional LSTM deep learning approach for COVID-19 forecasting.Scientific reports · 2023Article
- Healthcare Operations and Black Swan Event for COVID-19 Pandemic: A Predictive Analytics.IEEE transactions on engineering management · 2023Article
- A game theory-based COVID-19 close contact detecting method with edge computing collaboration.Computer communications · 2023Article
- A comprehensive systematic and bibliometric review of the IoT-based healthcare systems.Cluster computing · 2023Review
- Multivariate time series short term forecasting using cumulative data of coronavirus.Evolving systems · 2023Article
- Data quality model for assessing public COVID-19 big datasets.The Journal of supercomputing · 2023Article
- Exploring Future Signals of COVID-19 and Response to Information Diffusion Using Social Media Big Data.International journal of environmental research and public health · 2023Article
- Artificial Intelligence: A Next-Level Approach in Confronting the COVID-19 Pandemic.Healthcare (Basel, Switzerland) · 2023Review
- A coalitional game-based joint monitoring mechanism for combating COVID-19.Computer communications · 2023Article
- Using artificial intelligence to improve public health: a narrative review.Frontiers in public health · 2023Review
60 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The outbreak of COVID-19 Coronavirus, namely SARS-CoV-2, has created a calamitous situation throughout the world. The cumulative incidence of COVID-19 is rapidly increasing day by day. Machine Learning (ML) and Cloud Computing can be deployed very effectively to track the disease, predict growth of the epidemic and design strategies and policies to manage its spread. This study applies an improved mathematical model to analyse and predict the growth of the epidemic. An ML-based improved model has been applied to predict the potential threat of COVID-19 in countries worldwide. We show that using iterative weighting for fitting Generalized Inverse Weibull distribution, a better fit can be obtained to develop a prediction framework. This has been deployed on a cloud computing platform for more accurate and real-time prediction of the growth behavior of the epidemic. A data driven approach with higher accuracy as here can be very useful for a proactive response from the government and citizens. Finally, we propose a set of research opportunities and setup grounds for further practical applications.
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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.