Studying the effect of lockdown using epidemiological modelling of COVID-19 and a quantum computational approach using the Ising spin interaction.
Identifieur interne : 000835 ( Main/Corpus ); précédent : 000834; suivant : 000836Studying the effect of lockdown using epidemiological modelling of COVID-19 and a quantum computational approach using the Ising spin interaction.
Auteurs : Anshuman Padhi ; Sudev Pradhan ; Pragna Paramita Sahoo ; Kalyani Suresh ; Bikash K. Behera ; Prasanta K. PanigrahiSource :
- Scientific reports [ 2045-2322 ] ; 2020.
English descriptors
- KwdEn :
- MESH :
- epidemiology : COVID-19.
- prevention & control : COVID-19.
- Computer Simulation, Humans, Models, Biological, Pandemics, Quantum Theory, Quarantine, SARS-CoV-2.
Abstract
COVID-19 is a respiratory tract infection that can range from being mild to fatal. In India, the countrywide lockdown has been imposed since 24th march 2020, and has got multiple extensions with different guidelines for each phase. Among various models of epidemiology, we use the SIR(D) model to analyze the extent to which this multi-phased lockdown has been active in 'flattening the curve' and lower the threat. Analyzing the effect of lockdown on the infection may provide a better insight into the evolution of epidemic while implementing the quarantine procedures as well as improving the healthcare facilities. For accurate modelling, incorporating various parameters along with sophisticated computational facilities are required. Parallel to SIRD modelling, we tend to compare it with the Ising model and derive a quantum circuit that incorporates the rate of infection and rate of recovery, etc as its parameters. The probabilistic plots obtained from the circuit qualitatively resemble the shape of the curve for the spread of Coronavirus. We also demonstrate how the curve flattens when the lockdown is imposed. This kind of quantum computational approach can be useful in reducing space and time complexities of a huge amount of information related to the epidemic.
DOI: 10.1038/s41598-020-78652-0
PubMed: 33303815
PubMed Central: PMC7729942
Links to Exploration step
pubmed:33303815Le document en format XML
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Behera</name><affiliation><nlm:affiliation>Bikash's Quantum (OPC) Pvt. Ltd., Balindi, Mohanpur, Nadia, West Bengal, 741246, India. bikash@bikashsquantum.com.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Department of Physical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal, 741246, India. bikash@bikashsquantum.com.</nlm:affiliation></affiliation></author><author><name sortKey="Panigrahi, Prasanta K" sort="Panigrahi, Prasanta K" uniqKey="Panigrahi P" first="Prasanta K" last="Panigrahi">Prasanta K. Panigrahi</name><affiliation><nlm:affiliation>Department of Physical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal, 741246, India.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Scientific reports</title><idno type="eISSN">2045-2322</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>COVID-19 (epidemiology)</term><term>COVID-19 (prevention & control)</term><term>Computer Simulation (MeSH)</term><term>Humans (MeSH)</term><term>Models, Biological (MeSH)</term><term>Pandemics (MeSH)</term><term>Quantum Theory (MeSH)</term><term>Quarantine (MeSH)</term><term>SARS-CoV-2 (MeSH)</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>COVID-19</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>COVID-19</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Computer Simulation</term><term>Humans</term><term>Models, Biological</term><term>Pandemics</term><term>Quantum Theory</term><term>Quarantine</term><term>SARS-CoV-2</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">COVID-19 is a respiratory tract infection that can range from being mild to fatal. In India, the countrywide lockdown has been imposed since 24th march 2020, and has got multiple extensions with different guidelines for each phase. Among various models of epidemiology, we use the SIR(D) model to analyze the extent to which this multi-phased lockdown has been active in 'flattening the curve' and lower the threat. Analyzing the effect of lockdown on the infection may provide a better insight into the evolution of epidemic while implementing the quarantine procedures as well as improving the healthcare facilities. For accurate modelling, incorporating various parameters along with sophisticated computational facilities are required. Parallel to SIRD modelling, we tend to compare it with the Ising model and derive a quantum circuit that incorporates the rate of infection and rate of recovery, etc as its parameters. The probabilistic plots obtained from the circuit qualitatively resemble the shape of the curve for the spread of Coronavirus. We also demonstrate how the curve flattens when the lockdown is imposed. This kind of quantum computational approach can be useful in reducing space and time complexities of a huge amount of information related to the epidemic.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">33303815</PMID><DateCompleted><Year>2020</Year><Month>12</Month><Day>21</Day></DateCompleted><DateRevised><Year>2020</Year><Month>12</Month><Day>21</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">2045-2322</ISSN><JournalIssue CitedMedium="Internet"><Volume>10</Volume><Issue>1</Issue><PubDate><Year>2020</Year><Month>12</Month><Day>10</Day></PubDate></JournalIssue><Title>Scientific reports</Title><ISOAbbreviation>Sci Rep</ISOAbbreviation></Journal><ArticleTitle>Studying the effect of lockdown using epidemiological modelling of COVID-19 and a quantum computational approach using the Ising spin interaction.</ArticleTitle><Pagination><MedlinePgn>21741</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1038/s41598-020-78652-0</ELocationID><Abstract><AbstractText>COVID-19 is a respiratory tract infection that can range from being mild to fatal. In India, the countrywide lockdown has been imposed since 24th march 2020, and has got multiple extensions with different guidelines for each phase. Among various models of epidemiology, we use the SIR(D) model to analyze the extent to which this multi-phased lockdown has been active in 'flattening the curve' and lower the threat. Analyzing the effect of lockdown on the infection may provide a better insight into the evolution of epidemic while implementing the quarantine procedures as well as improving the healthcare facilities. For accurate modelling, incorporating various parameters along with sophisticated computational facilities are required. Parallel to SIRD modelling, we tend to compare it with the Ising model and derive a quantum circuit that incorporates the rate of infection and rate of recovery, etc as its parameters. The probabilistic plots obtained from the circuit qualitatively resemble the shape of the curve for the spread of Coronavirus. We also demonstrate how the curve flattens when the lockdown is imposed. 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Ltd., Balindi, Mohanpur, Nadia, West Bengal, 741246, India. bikash@bikashsquantum.com.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Physical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal, 741246, India. bikash@bikashsquantum.com.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Panigrahi</LastName><ForeName>Prasanta K</ForeName><Initials>PK</Initials><Identifier Source="ORCID">0000-0001-5812-0353</Identifier><AffiliationInfo><Affiliation>Department of Physical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal, 741246, India.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>12</Month><Day>10</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Sci Rep</MedlineTA><NlmUniqueID>101563288</NlmUniqueID><ISSNLinking>2045-2322</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000086382" MajorTopicYN="Y">COVID-19</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000517" MajorTopicYN="N">prevention & control</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003198" MajorTopicYN="Y">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008954" MajorTopicYN="Y">Models, Biological</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="Y">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011789" MajorTopicYN="N">Quantum Theory</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011790" MajorTopicYN="Y">Quarantine</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000086402" MajorTopicYN="Y">SARS-CoV-2</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>06</Month><Day>03</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>10</Month><Day>01</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>12</Month><Day>11</Day><Hour>5</Hour><Minute>54</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>12</Month><Day>12</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>12</Month><Day>22</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">33303815</ArticleId><ArticleId IdType="doi">10.1038/s41598-020-78652-0</ArticleId><ArticleId IdType="pii">10.1038/s41598-020-78652-0</ArticleId><ArticleId IdType="pmc">PMC7729942</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Bull Math Biol. 2013 Oct;75(10):1716-46</Citation><ArticleIdList><ArticleId IdType="pubmed">23797790</ArticleId></ArticleIdList></Reference><Reference><Citation>J Adv Res. 2020 Mar 16;24:91-98</Citation><ArticleIdList><ArticleId IdType="pubmed">32257431</ArticleId></ArticleIdList></Reference><Reference><Citation>Eur J Oper Res. 2011 Dec 16;215(3):679-687</Citation><ArticleIdList><ArticleId IdType="pubmed">21966083</ArticleId></ArticleIdList></Reference><Reference><Citation>Science. 2020 May 1;368(6490):489-493</Citation><ArticleIdList><ArticleId IdType="pubmed">32179701</ArticleId></ArticleIdList></Reference><Reference><Citation>Ann Intern Med. 2020 May 5;172(9):577-582</Citation><ArticleIdList><ArticleId IdType="pubmed">32150748</ArticleId></ArticleIdList></Reference><Reference><Citation>Sci Rep. 2018 Oct 4;8(1):14793</Citation><ArticleIdList><ArticleId IdType="pubmed">30287854</ArticleId></ArticleIdList></Reference><Reference><Citation>J Epidemiol Glob Health. 2020 Aug 28;:</Citation><ArticleIdList><ArticleId IdType="pubmed">32959618</ArticleId></ArticleIdList></Reference><Reference><Citation>J Phys Chem B. 2018 Apr 5;122(13):3384-3395</Citation><ArticleIdList><ArticleId IdType="pubmed">29099600</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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