An intelligent system for predicting and preventing MERS-CoV infection outbreak.
Identifieur interne : 001223 ( PubMed/Checkpoint ); précédent : 001222; suivant : 001224An intelligent system for predicting and preventing MERS-CoV infection outbreak.
Auteurs : Rajinder Sandhu ; Sandeep K. Sood ; Gurpreet KaurSource :
- The Journal of supercomputing [ 0920-8542 ] ; 2016.
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.
DOI: 10.1007/s11227-015-1474-0
PubMed: 32214655
Affiliations:
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Sood</name><affiliation><nlm:affiliation>Computer Science and Engineering Department, Guru Nanak Dev University, Regional Campus, Gurdaspur, Punjab India.</nlm:affiliation><wicri:noCountry code="subField">Punjab India</wicri:noCountry></affiliation></author><author><name sortKey="Kaur, Gurpreet" sort="Kaur, Gurpreet" uniqKey="Kaur G" first="Gurpreet" last="Kaur">Gurpreet Kaur</name><affiliation><nlm:affiliation>Computer Science and Engineering Department, Guru Nanak Dev University, Regional Campus, Gurdaspur, Punjab India.</nlm:affiliation><wicri:noCountry code="subField">Punjab India</wicri:noCountry></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2016">2016</date><idno type="RBID">pubmed:32214655</idno><idno type="pmid">32214655</idno><idno type="doi">10.1007/s11227-015-1474-0</idno><idno type="wicri:Area/PubMed/Corpus">001317</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001317</idno><idno type="wicri:Area/PubMed/Curation">001317</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">001317</idno><idno type="wicri:Area/PubMed/Checkpoint">001223</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">001223</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">An intelligent system for predicting and preventing MERS-CoV infection outbreak.</title><author><name sortKey="Sandhu, Rajinder" sort="Sandhu, Rajinder" uniqKey="Sandhu R" first="Rajinder" last="Sandhu">Rajinder Sandhu</name><affiliation><nlm:affiliation>Computer Science and Engineering Department, Guru Nanak Dev University, Regional Campus, Gurdaspur, Punjab India.</nlm:affiliation><wicri:noCountry code="subField">Punjab India</wicri:noCountry></affiliation></author><author><name sortKey="Sood, Sandeep K" sort="Sood, Sandeep K" uniqKey="Sood S" first="Sandeep K" last="Sood">Sandeep K. Sood</name><affiliation><nlm:affiliation>Computer Science and Engineering Department, Guru Nanak Dev University, Regional Campus, Gurdaspur, Punjab India.</nlm:affiliation><wicri:noCountry code="subField">Punjab India</wicri:noCountry></affiliation></author><author><name sortKey="Kaur, Gurpreet" sort="Kaur, Gurpreet" uniqKey="Kaur G" first="Gurpreet" last="Kaur">Gurpreet Kaur</name><affiliation><nlm:affiliation>Computer Science and Engineering Department, Guru Nanak Dev University, Regional Campus, Gurdaspur, Punjab India.</nlm:affiliation><wicri:noCountry code="subField">Punjab India</wicri:noCountry></affiliation></author></analytic><series><title level="j">The Journal of supercomputing</title><idno type="ISSN">0920-8542</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32214655</PMID><DateRevised><Year>2020</Year><Month>03</Month><Day>31</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0920-8542</ISSN><JournalIssue CitedMedium="Print"><Volume>72</Volume><Issue>8</Issue><PubDate><Year>2016</Year></PubDate></JournalIssue><Title>The Journal of supercomputing</Title><ISOAbbreviation>J Supercomput</ISOAbbreviation></Journal><ArticleTitle>An intelligent system for predicting and preventing MERS-CoV infection outbreak.</ArticleTitle><Pagination><MedlinePgn>3033-3056</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1007/s11227-015-1474-0</ELocationID><Abstract><AbstractText>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.</AbstractText><CopyrightInformation>© Springer Science+Business Media New York 2015.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Sandhu</LastName><ForeName>Rajinder</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Computer Science and Engineering Department, Guru Nanak Dev University, Regional Campus, Gurdaspur, Punjab India.</Affiliation><Identifier Source="GRID">grid.411894.1</Identifier><Identifier Source="ISNI">0000000107268286</Identifier></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sood</LastName><ForeName>Sandeep K</ForeName><Initials>SK</Initials><AffiliationInfo><Affiliation>Computer Science and Engineering Department, Guru Nanak Dev University, Regional Campus, Gurdaspur, Punjab India.</Affiliation><Identifier Source="GRID">grid.411894.1</Identifier><Identifier Source="ISNI">0000000107268286</Identifier></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kaur</LastName><ForeName>Gurpreet</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>Computer Science and Engineering Department, Guru Nanak Dev University, Regional Campus, Gurdaspur, Punjab India.</Affiliation><Identifier Source="GRID">grid.411894.1</Identifier><Identifier Source="ISNI">0000000107268286</Identifier></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2015</Year><Month>07</Month><Day>08</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>J Supercomput</MedlineTA><NlmUniqueID>9889997</NlmUniqueID><ISSNLinking>0920-8542</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Bayesian belief network</Keyword><Keyword MajorTopicYN="N">Big data</Keyword><Keyword MajorTopicYN="N">Cloud computing</Keyword><Keyword MajorTopicYN="N">Geographic positioning system (GPS)</Keyword><Keyword MajorTopicYN="N">Information granulation</Keyword><Keyword MajorTopicYN="N">MERS-CoV</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>3</Month><Day>28</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2016</Year><Month>1</Month><Day>1</Day><Hour>0</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2016</Year><Month>1</Month><Day>1</Day><Hour>0</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32214655</ArticleId><ArticleId IdType="doi">10.1007/s11227-015-1474-0</ArticleId><ArticleId IdType="pii">1474</ArticleId><ArticleId IdType="pmc">PMC7089482</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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