Predicting optimal lockdown period with parametric approach using three-phase maturation SIRD model for COVID-19 pandemic.
Identifieur interne : 001297 ( Main/Exploration ); précédent : 001296; suivant : 001298Predicting optimal lockdown period with parametric approach using three-phase maturation SIRD model for COVID-19 pandemic.
Auteurs : Soniya Lalwani [Inde] ; Gunjan Sahni [Inde] ; Bhawna Mewara [Inde] ; Rajesh Kumar [Inde]Source :
- Chaos, solitons, and fractals [ 0960-0779 ] ; 2020.
Abstract
This paper proposes a three-phase Susceptible-Infected-Recovered-Dead (3P-SIRD) model to calculate an optimal lockdown period for some specific geographical regions that will be favorable to break not only the transmission chain but also will help country's economy to recover and support infrastructure in a fight against COVID-19. Proposed model is novel since it additionally includes parameters i.e. silent carriers, sociability of newly infected person and unregistered died coronavirus infected people along with the infection rate, suspected rate and death rate. These parameters contribute a lot to figure out the more clear model, along with essential parameters. The model takes the testing rate of suspected people into consideration and this rate varies with respect to phase of the epidemic growth. Proposed 3P-SIRD model is divided into three-phases based on the awareness and sustainability of disease. Time is divided into different periods as rate of infection and recovery fluctuates region to region. The model is tested on China data and is efficient enough to propose a model very close to their actual figures of infected people, recovered people, died and active cases. The model predicts the optimal lockdown period as 73 days for China which is very close to their actual lockdown period (77 days). Further, the model is implemented to predict the optimal lockdown period of India and Italy.
DOI: 10.1016/j.chaos.2020.109939
PubMed: 32834574
PubMed Central: PMC7260573
Affiliations:
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pandemic.</title><author><name sortKey="Lalwani, Soniya" sort="Lalwani, Soniya" uniqKey="Lalwani S" first="Soniya" last="Lalwani">Soniya Lalwani</name><affiliation wicri:level="1"><nlm:affiliation>Department of Mathematics, Bal Krishna Institute of Technology, Kota, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Mathematics, Bal Krishna Institute of Technology, Kota</wicri:regionArea><wicri:noRegion>Kota</wicri:noRegion></affiliation></author><author><name sortKey="Sahni, Gunjan" sort="Sahni, Gunjan" uniqKey="Sahni G" first="Gunjan" last="Sahni">Gunjan Sahni</name><affiliation wicri:level="1"><nlm:affiliation>Department of Computer Science and Engineering, Career Point University, Kota, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Computer Science and Engineering, Career Point University, Kota</wicri:regionArea><wicri:noRegion>Kota</wicri:noRegion></affiliation></author><author><name sortKey="Mewara, Bhawna" sort="Mewara, Bhawna" uniqKey="Mewara B" first="Bhawna" last="Mewara">Bhawna Mewara</name><affiliation wicri:level="1"><nlm:affiliation>Department of Computer Science and Engineering, Career Point University, Kota, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Computer Science and Engineering, Career Point University, Kota</wicri:regionArea><wicri:noRegion>Kota</wicri:noRegion></affiliation></author><author><name sortKey="Kumar, Rajesh" sort="Kumar, Rajesh" uniqKey="Kumar R" first="Rajesh" last="Kumar">Rajesh Kumar</name><affiliation wicri:level="1"><nlm:affiliation>Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur</wicri:regionArea><wicri:noRegion>Jaipur</wicri:noRegion></affiliation></author></analytic><series><title level="j">Chaos, solitons, and fractals</title><idno type="ISSN">0960-0779</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">This paper proposes a three-phase Susceptible-Infected-Recovered-Dead (3P-SIRD) model to calculate an optimal lockdown period for some specific geographical regions that will be favorable to break not only the transmission chain but also will help country's economy to recover and support infrastructure in a fight against COVID-19. Proposed model is novel since it additionally includes parameters <i>i.e.</i> silent carriers, sociability of newly infected person and unregistered died coronavirus infected people along with the infection rate, suspected rate and death rate. These parameters contribute a lot to figure out the more clear model, along with essential parameters. The model takes the testing rate of suspected people into consideration and this rate varies with respect to phase of the epidemic growth. Proposed 3P-SIRD model is divided into three-phases based on the awareness and sustainability of disease. Time is divided into different periods as rate of infection and recovery fluctuates region to region. The model is tested on China data and is efficient enough to propose a model very close to their actual figures of infected people, recovered people, died and active cases. The model predicts the optimal lockdown period as 73 days for China which is very close to their actual lockdown period (77 days). Further, the model is implemented to predict the optimal lockdown period of India and Italy.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32834574</PMID><DateRevised><Year>2020</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0960-0779</ISSN><JournalIssue CitedMedium="Print"><Volume>138</Volume><PubDate><Year>2020</Year><Month>Sep</Month></PubDate></JournalIssue><Title>Chaos, solitons, and fractals</Title><ISOAbbreviation>Chaos Solitons Fractals</ISOAbbreviation></Journal><ArticleTitle>Predicting optimal lockdown period with parametric approach using three-phase maturation SIRD model for COVID-19 pandemic.</ArticleTitle><Pagination><MedlinePgn>109939</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.chaos.2020.109939</ELocationID><Abstract><AbstractText>This paper proposes a three-phase Susceptible-Infected-Recovered-Dead (3P-SIRD) model to calculate an optimal lockdown period for some specific geographical regions that will be favorable to break not only the transmission chain but also will help country's economy to recover and support infrastructure in a fight against COVID-19. Proposed model is novel since it additionally includes parameters <i>i.e.</i> silent carriers, sociability of newly infected person and unregistered died coronavirus infected people along with the infection rate, suspected rate and death rate. These parameters contribute a lot to figure out the more clear model, along with essential parameters. The model takes the testing rate of suspected people into consideration and this rate varies with respect to phase of the epidemic growth. Proposed 3P-SIRD model is divided into three-phases based on the awareness and sustainability of disease. Time is divided into different periods as rate of infection and recovery fluctuates region to region. The model is tested on China data and is efficient enough to propose a model very close to their actual figures of infected people, recovered people, died and active cases. The model predicts the optimal lockdown period as 73 days for China which is very close to their actual lockdown period (77 days). Further, the model is implemented to predict the optimal lockdown period of India and Italy.</AbstractText><CopyrightInformation>© 2020 Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Lalwani</LastName><ForeName>Soniya</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Department of Mathematics, Bal Krishna Institute of Technology, Kota, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sahni</LastName><ForeName>Gunjan</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>Department of Computer Science and Engineering, Career Point University, Kota, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mewara</LastName><ForeName>Bhawna</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>Department of Computer Science and Engineering, Career Point University, Kota, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kumar</LastName><ForeName>Rajesh</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, India.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>05</Month><Day>30</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Chaos Solitons Fractals</MedlineTA><NlmUniqueID>100971564</NlmUniqueID><ISSNLinking>0960-0779</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">Lockdown</Keyword><Keyword MajorTopicYN="N">Pandemic</Keyword><Keyword MajorTopicYN="N">SIRD Model</Keyword><Keyword MajorTopicYN="N">Silent Carriers</Keyword></KeywordList><CoiStatement>The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>04</Month><Day>29</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>05</Month><Day>26</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">32834574</ArticleId><ArticleId IdType="doi">10.1016/j.chaos.2020.109939</ArticleId><ArticleId IdType="pii">S0960-0779(20)30338-6</ArticleId><ArticleId IdType="pii">109939</ArticleId><ArticleId IdType="pmc">PMC7260573</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
<ReferenceList><Reference><Citation>Lancet. 2020 Feb 29;395(10225):689-697</Citation><ArticleIdList><ArticleId IdType="pubmed">32014114</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet Infect Dis. 2020 May;20(5):553-558</Citation><ArticleIdList><ArticleId IdType="pubmed">32171059</ArticleId></ArticleIdList></Reference><Reference><Citation>Chaos Solitons Fractals. 2020 May;134:109761</Citation><ArticleIdList><ArticleId IdType="pubmed">32308258</ArticleId></ArticleIdList></Reference><Reference><Citation>Int J Infect Dis. 2020 Feb;91:264-266</Citation><ArticleIdList><ArticleId IdType="pubmed">31953166</ArticleId></ArticleIdList></Reference><Reference><Citation>Euro Surveill. 2020 Apr;25(13):</Citation><ArticleIdList><ArticleId IdType="pubmed">32265005</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Inde</li></country></list><tree><country name="Inde"><noRegion><name sortKey="Lalwani, Soniya" sort="Lalwani, Soniya" uniqKey="Lalwani S" first="Soniya" last="Lalwani">Soniya Lalwani</name></noRegion><name sortKey="Kumar, Rajesh" sort="Kumar, Rajesh" uniqKey="Kumar R" first="Rajesh" last="Kumar">Rajesh Kumar</name><name sortKey="Mewara, Bhawna" sort="Mewara, Bhawna" uniqKey="Mewara B" first="Bhawna" last="Mewara">Bhawna Mewara</name><name sortKey="Sahni, Gunjan" sort="Sahni, Gunjan" uniqKey="Sahni G" first="Gunjan" last="Sahni">Gunjan Sahni</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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