An intelligent system for predicting and preventing MERS-CoV infection outbreak
Identifieur interne : 000737 ( new/Analysis ); précédent : 000736; suivant : 000738An intelligent system for predicting and preventing MERS-CoV infection outbreak
Auteurs : Rajinder Sandhu ; Sandeep K. Sood ; Gurpreet KaurSource :
- The Journal of Supercomputing [ 0920-8542 ] ; 2015.
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.
Url:
DOI: 10.1007/s11227-015-1474-0
PubMed: NONE
PubMed Central: 7089482
Affiliations:
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PMC:7089482Le document en format XML
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Sood</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Kaur, Gurpreet" sort="Kaur, Gurpreet" uniqKey="Kaur G" first="Gurpreet" last="Kaur">Gurpreet Kaur</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">The Journal of Supercomputing</title><idno type="ISSN">0920-8542</idno><idno type="eISSN">1573-0484</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="Par1">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. 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