ArticleThe Journal of supercomputing2016
An intelligent system for predicting and preventing MERS-CoV infection outbreak.
Article in The Journal of supercomputing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review.Journal of medical systems · 2020Pooled it
- Digital epidemiology: harnessing big data for early detection and monitoring of viral outbreaks.Infection prevention in practice · 2024Review
- Improved Survival Analyses Based on Characterized Time-Dependent Covariates to Predict Individual Chronic Kidney Disease Progression.Biomedicines · 2023Article
- Artificial Intelligence: A Next-Level Approach in Confronting the COVID-19 Pandemic.Healthcare (Basel, Switzerland) · 2023Review
- A visual review of artificial intelligence and Industry 4.0 in healthcare.Computers & electrical engineering : an international journal · 2022Article
- Diagnosing, Managing, and Controlling COVID-19 using Clinical Decision Support Systems: A Study to Introduce CDSS Applications.Journal of biomedical physics & engineering · 2022Article
- How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future.Reviews in medical virology · 2021Review
- Scoping future outbreaks: a scoping review on the outbreak prediction of the WHO Blueprint list of priority diseases.BMJ global health · 2021Article
- Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance.Journal of biomedical informatics · 2021Article
- IoMT amid COVID-19 pandemic: Application, architecture, technology, and security.Journal of network and computer applications (Online) · 2021Review
- An Intelligent and Energy-Efficient Wireless Body Area Network to Control Coronavirus Outbreak.Arabian journal for science and engineering · 2021Article
- A re-organizing biosurveillance framework based on fog and mobile edge computing.Multimedia tools and applications · 2021Article
- CASE-CF: Context Aware Smart Epidemic Control Framework.New generation computing · 2021Article
- Digital Response During the COVID-19 Pandemic in Saudi Arabia.Journal of medical Internet research · 2020Article
- Big data analytics for preventive medicine.Neural computing & applications · 2020Article
- COVID-19 & an NGO and university developed interactive portal: a perspective from Iran.Health and technology · 2020Article
- Kyasanur Forest Disease Classification Framework Using Novel Extremal Optimization Tuned Neural Network in Fog Computing Environment.Journal of medical systems · 2018Article
- Wearable IoT sensor based healthcare system for identifying and controlling chikungunya virus.Computers in industry · 2017Article
- Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing.Journal of computational science · 2016Article
- Building predictive models for MERS-CoV infections using data mining techniques.Journal of infection and public healthArticle
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 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MERS-CoV is an airborne disease which spreads easily and has high death rate. To predict and prevent MERS-CoV, real-time analysis of user's health data and his/her geographic location are fundamental. Development of healthcare systems using cloud computing is emerging as an effective solution having benefits of better quality of service, reduced cost, scalability, and flexibility. In this paper, an effective cloud computing system is proposed which predicts MERS-CoV-infected patients using Bayesian belief network and provides geographic-based risk assessment to control its outbreak. The proposed system is tested on synthetic data generated for 0.2 million users. System provided high accuracy for classification and appropriate geographic-based risk assessment. The key point of this paper is the use of geographic positioning system to represent each MERS-CoV users on Google maps so that possibly infected users can be quarantined as early as possible. It will help uninfected citizens to avoid regional exposure and the government agencies to manage the problem more effectively.
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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.