Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices.
Identifieur interne : 001B57 ( Main/Corpus ); précédent : 001B56; suivant : 001B58Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices.
Auteurs : Md Arafatur Rahman ; Nafees Zaman ; A Taufiq Asyhari ; Fadi Al-Turjman ; Md Zakirul Alam Bhuiyan ; M F ZolkipliSource :
- Sustainable cities and society [ 2210-6715 ] ; 2020.
Abstract
The COVID-19 disease has once again reiterated the impact of pandemics beyond a biomedical event with potential rapid, dramatic, sweeping disruptions to the management, and conduct of everyday life. Not only the rate and pattern of contagion that threaten our sense of healthy living but also the safety measures put in place for containing the spread of the virus may require social distancing. Three different measures to counteract this pandemic situation have emerged, namely: (i) vaccination, (ii) herd immunity development, and (iii) lockdown. As the first measure is not ready at this stage and the second measure is largely considered unreasonable on the account of the gigantic number of fatalities, a vast majority of countries have practiced the third option despite having a potentially immense adverse economic impact. To mitigate such an impact, this paper proposes a data-driven dynamic clustering framework for moderating the adverse economic impact of COVID-19 flare-up. Through an intelligent fusion of healthcare and simulated mobility data, we model lockdown as a clustering problem and design a dynamic clustering algorithm for localized lockdown by taking into account the pandemic, economic and mobility aspects. We then validate the proposed algorithms by conducting extensive simulations using the Malaysian context as a case study. The findings signify the promises of dynamic clustering for lockdown coverage reduction, reduced economic loss, and military unit deployment reduction, as well as assess potential impact of uncooperative civilians on the contamination rate. The outcome of this work is anticipated to pave a way for significantly reducing the severe economic impact of the COVID-19 spreading. Moreover, the idea can be exploited for potentially the next waves of corona virus-related diseases and other upcoming viral life-threatening calamities.
DOI: 10.1016/j.scs.2020.102372
PubMed: 32834935
PubMed Central: PMC7333601
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices.</title><author><name sortKey="Rahman, Md Arafatur" sort="Rahman, Md Arafatur" uniqKey="Rahman M" first="Md Arafatur" last="Rahman">Md Arafatur Rahman</name><affiliation><nlm:affiliation>Faculty of Computing, University Malaysia Pahang, Gambang 26300, Malaysia.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>IBM CoE, Malaysia.</nlm:affiliation></affiliation></author><author><name sortKey="Zaman, Nafees" sort="Zaman, Nafees" uniqKey="Zaman N" first="Nafees" last="Zaman">Nafees Zaman</name><affiliation><nlm:affiliation>IBM CoE, Malaysia.</nlm:affiliation></affiliation></author><author><name sortKey="Asyhari, A Taufiq" sort="Asyhari, A Taufiq" uniqKey="Asyhari A" first="A Taufiq" last="Asyhari">A Taufiq Asyhari</name><affiliation><nlm:affiliation>School of Computing and Digital Technology, Birmingham City University, Millennium Point, Birmingham, B4 7XG, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Al Turjman, Fadi" sort="Al Turjman, Fadi" uniqKey="Al Turjman F" first="Fadi" last="Al-Turjman">Fadi Al-Turjman</name><affiliation><nlm:affiliation>Research Center for AI and IoT, Near East University, 99138 Nicosia, Turkey.</nlm:affiliation></affiliation></author><author><name sortKey="Alam Bhuiyan, Md Zakirul" sort="Alam Bhuiyan, Md Zakirul" uniqKey="Alam Bhuiyan M" first="Md Zakirul" last="Alam Bhuiyan">Md Zakirul Alam Bhuiyan</name><affiliation><nlm:affiliation>Department of Computer and Information Sciences, Fordham University, New York, NY 10458, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Zolkipli, M F" sort="Zolkipli, M F" uniqKey="Zolkipli M" first="M F" last="Zolkipli">M F Zolkipli</name><affiliation><nlm:affiliation>Faculty of Computing, University Malaysia Pahang, Gambang 26300, Malaysia.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32834935</idno><idno type="pmid">32834935</idno><idno type="doi">10.1016/j.scs.2020.102372</idno><idno type="pmc">PMC7333601</idno><idno type="wicri:Area/Main/Corpus">001B57</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">001B57</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices.</title><author><name sortKey="Rahman, Md Arafatur" sort="Rahman, Md Arafatur" uniqKey="Rahman M" first="Md Arafatur" last="Rahman">Md Arafatur Rahman</name><affiliation><nlm:affiliation>Faculty of Computing, University Malaysia Pahang, Gambang 26300, Malaysia.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>IBM CoE, Malaysia.</nlm:affiliation></affiliation></author><author><name sortKey="Zaman, Nafees" sort="Zaman, Nafees" uniqKey="Zaman N" first="Nafees" last="Zaman">Nafees Zaman</name><affiliation><nlm:affiliation>IBM CoE, Malaysia.</nlm:affiliation></affiliation></author><author><name sortKey="Asyhari, A Taufiq" sort="Asyhari, A Taufiq" uniqKey="Asyhari A" first="A Taufiq" last="Asyhari">A Taufiq Asyhari</name><affiliation><nlm:affiliation>School of Computing and Digital Technology, Birmingham City University, Millennium Point, Birmingham, B4 7XG, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Al Turjman, Fadi" sort="Al Turjman, Fadi" uniqKey="Al Turjman F" first="Fadi" last="Al-Turjman">Fadi Al-Turjman</name><affiliation><nlm:affiliation>Research Center for AI and IoT, Near East University, 99138 Nicosia, Turkey.</nlm:affiliation></affiliation></author><author><name sortKey="Alam Bhuiyan, Md Zakirul" sort="Alam Bhuiyan, Md Zakirul" uniqKey="Alam Bhuiyan M" first="Md Zakirul" last="Alam Bhuiyan">Md Zakirul Alam Bhuiyan</name><affiliation><nlm:affiliation>Department of Computer and Information Sciences, Fordham University, New York, NY 10458, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Zolkipli, M F" sort="Zolkipli, M F" uniqKey="Zolkipli M" first="M F" last="Zolkipli">M F Zolkipli</name><affiliation><nlm:affiliation>Faculty of Computing, University Malaysia Pahang, Gambang 26300, Malaysia.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Sustainable cities and society</title><idno type="eISSN">2210-6715</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The COVID-19 disease has once again reiterated the impact of pandemics beyond a biomedical event with potential rapid, dramatic, sweeping disruptions to the management, and conduct of everyday life. Not only the rate and pattern of contagion that threaten our sense of healthy living but also the safety measures put in place for containing the spread of the virus may require social distancing. Three different measures to counteract this pandemic situation have emerged, namely: (i) vaccination, (ii) herd immunity development, and (iii) lockdown. As the first measure is not ready at this stage and the second measure is largely considered unreasonable on the account of the gigantic number of fatalities, a vast majority of countries have practiced the third option despite having a potentially immense adverse economic impact. To mitigate such an impact, this paper proposes a data-driven dynamic clustering framework for moderating the adverse economic impact of COVID-19 flare-up. Through an intelligent fusion of healthcare and simulated mobility data, we model lockdown as a clustering problem and design a dynamic clustering algorithm for localized lockdown by taking into account the pandemic, economic and mobility aspects. We then validate the proposed algorithms by conducting extensive simulations using the Malaysian context as a case study. The findings signify the promises of dynamic clustering for lockdown coverage reduction, reduced economic loss, and military unit deployment reduction, as well as assess potential impact of uncooperative civilians on the contamination rate. The outcome of this work is anticipated to pave a way for significantly reducing the severe economic impact of the COVID-19 spreading. Moreover, the idea can be exploited for potentially the next waves of corona virus-related diseases and other upcoming viral life-threatening calamities.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32834935</PMID><DateRevised><Year>2020</Year><Month>09</Month><Day>28</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">2210-6715</ISSN><JournalIssue CitedMedium="Internet"><Volume>62</Volume><PubDate><Year>2020</Year><Month>Nov</Month></PubDate></JournalIssue><Title>Sustainable cities and society</Title><ISOAbbreviation>Sustain Cities Soc</ISOAbbreviation></Journal><ArticleTitle>Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices.</ArticleTitle><Pagination><MedlinePgn>102372</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.scs.2020.102372</ELocationID><Abstract><AbstractText>The COVID-19 disease has once again reiterated the impact of pandemics beyond a biomedical event with potential rapid, dramatic, sweeping disruptions to the management, and conduct of everyday life. Not only the rate and pattern of contagion that threaten our sense of healthy living but also the safety measures put in place for containing the spread of the virus may require social distancing. Three different measures to counteract this pandemic situation have emerged, namely: (i) vaccination, (ii) herd immunity development, and (iii) lockdown. As the first measure is not ready at this stage and the second measure is largely considered unreasonable on the account of the gigantic number of fatalities, a vast majority of countries have practiced the third option despite having a potentially immense adverse economic impact. To mitigate such an impact, this paper proposes a data-driven dynamic clustering framework for moderating the adverse economic impact of COVID-19 flare-up. Through an intelligent fusion of healthcare and simulated mobility data, we model lockdown as a clustering problem and design a dynamic clustering algorithm for localized lockdown by taking into account the pandemic, economic and mobility aspects. We then validate the proposed algorithms by conducting extensive simulations using the Malaysian context as a case study. The findings signify the promises of dynamic clustering for lockdown coverage reduction, reduced economic loss, and military unit deployment reduction, as well as assess potential impact of uncooperative civilians on the contamination rate. The outcome of this work is anticipated to pave a way for significantly reducing the severe economic impact of the COVID-19 spreading. Moreover, the idea can be exploited for potentially the next waves of corona virus-related diseases and other upcoming viral life-threatening calamities.</AbstractText><CopyrightInformation>© 2020 Published by Elsevier Ltd.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Rahman</LastName><ForeName>Md Arafatur</ForeName><Initials>MA</Initials><AffiliationInfo><Affiliation>Faculty of Computing, University Malaysia Pahang, Gambang 26300, Malaysia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>IBM CoE, Malaysia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zaman</LastName><ForeName>Nafees</ForeName><Initials>N</Initials><AffiliationInfo><Affiliation>IBM CoE, Malaysia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Asyhari</LastName><ForeName>A Taufiq</ForeName><Initials>AT</Initials><AffiliationInfo><Affiliation>School of Computing and Digital Technology, Birmingham City University, Millennium Point, Birmingham, B4 7XG, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Al-Turjman</LastName><ForeName>Fadi</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Research Center for AI and IoT, Near East University, 99138 Nicosia, Turkey.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Alam Bhuiyan</LastName><ForeName>Md Zakirul</ForeName><Initials>MZ</Initials><AffiliationInfo><Affiliation>Department of Computer and Information Sciences, Fordham University, New York, NY 10458, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zolkipli</LastName><ForeName>M F</ForeName><Initials>MF</Initials><AffiliationInfo><Affiliation>Faculty of Computing, University Malaysia Pahang, Gambang 26300, Malaysia.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>07</Month><Day>03</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Sustain Cities Soc</MedlineTA><NlmUniqueID>101735304</NlmUniqueID><ISSNLinking>2210-6707</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Covid-19</Keyword><Keyword MajorTopicYN="N">Dynamic clustering</Keyword><Keyword MajorTopicYN="N">Lockdown</Keyword><Keyword MajorTopicYN="N">Pandemic</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>05</Month><Day>09</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>06</Month><Day>29</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>06</Month><Day>30</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32834935</ArticleId><ArticleId IdType="doi">10.1016/j.scs.2020.102372</ArticleId><ArticleId IdType="pii">S2210-6707(20)30593-X</ArticleId><ArticleId IdType="pii">102372</ArticleId><ArticleId IdType="pmc">PMC7333601</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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