ArticleJournal of computational science2016
Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing.
Article in Journal of computational science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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.
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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
9 citing papers in PubMed.
- Investigating the performance of machine learning algorithms in predicting the survival of COVID-19 patients: A cross section study of Iran.Health science reports · 2023Article
- ICT Enabled Disease Diagnosis, Treatment and Management-A Holistic Cost-Effective Approach Through Data Management and Analysis in UAE and India.Frontiers in artificial intelligence · 2022Review
- Maintaining proper health records improves machine learning predictions for novel 2019-nCoV.BMC medical informatics and decision making · 2021Article
- A re-organizing biosurveillance framework based on fog and mobile edge computing.Multimedia tools and applications · 2021Article
- Information technology in emergency management of COVID-19 outbreak.Informatics in medicine unlocked · 2020Review
- A study on medical Internet of Things and Big Data in personalized healthcare system.Health information science and systems · 2018Article
- 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
- Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 Pandemic.Journal of oral biology and craniofacial researchArticle
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
H1N1 is an infectious virus which, when spread affects a large volume of the population. It is an airborne disease that spreads easily and has a high death rate. Development of healthcare support systems using cloud computing is emerging as an effective solution with the benefits of better quality of service, reduced costs and flexibility. In this paper, an effective cloud computing architecture is proposed which predicts H1N1 infected patients and provides preventions to control infection rate. It consists of four processing components along with secure cloud storage medical database. The random decision tree is used to initially assess the infection in any patient depending on his/her symptoms. Social Network Analysis (SNA) is used to present the state of the outbreak. The proposed architecture is tested on synthetic data generated for two million users. The system provided 94% accuracy for the classification and around 81% of the resource utilization on Amazon EC2 cloud. The key point of the paper is the use of SNA graphs to calculate role of an infected user in spreading the outbreak known as Outbreak Role Index (ORI). It will help government agencies and healthcare departments to present, analyze and prevent outbreak effectively.
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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.