On a Coupled Time-Dependent SIR Models Fitting with New York and New-Jersey States COVID-19 Data.
Identifieur interne : 000A22 ( Main/Exploration ); précédent : 000A21; suivant : 000A23On a Coupled Time-Dependent SIR Models Fitting with New York and New-Jersey States COVID-19 Data.
Auteurs : Benjamin Ambrosio [France] ; M A Aziz-Alaoui [France]Source :
- Biology [ 2079-7737 ] ; 2020.
Abstract
This article describes a simple Susceptible Infected Recovered (SIR) model fitting with COVID-19 data for the month of March 2020 in New York (NY) state. The model is a classical SIR, but is non-autonomous; the rate of susceptible people becoming infected is adjusted over time in order to fit the available data. The death rate is also secondarily adjusted. Our fitting is made under the assumption that due to limiting number of tests, a large part of the infected population has not been tested positive. In the last part, we extend the model to take into account the daily fluxes between New Jersey (NJ) and NY states and fit the data for both states. Our simple model fits the available data, and illustrates typical dynamics of the disease: exponential increase, apex and decrease. The model highlights a decrease in the transmission rate over the period which gives a quantitative illustration about how lockdown policies reduce the spread of the pandemic. The coupled model with NY and NJ states shows a wave in NJ following the NY wave, illustrating the mechanism of spread from one attractive hot spot to its neighbor.
DOI: 10.3390/biology9060135
PubMed: 32599867
PubMed Central: PMC7344619
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">On a Coupled Time-Dependent SIR Models Fitting with New York and New-Jersey States COVID-19 Data.</title><author><name sortKey="Ambrosio, Benjamin" sort="Ambrosio, Benjamin" uniqKey="Ambrosio B" first="Benjamin" last="Ambrosio">Benjamin Ambrosio</name><affiliation wicri:level="3"><nlm:affiliation>UNIHAVRE, LMAH, FR-CNRS-3335, ISCN, Normandie University, 76600 Le Havre, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>UNIHAVRE, LMAH, FR-CNRS-3335, ISCN, Normandie University, 76600 Le Havre</wicri:regionArea><placeName><region type="region" nuts="2">Région Normandie</region><region type="old region" nuts="2">Haute-Normandie</region><settlement type="city">Le Havre</settlement></placeName></affiliation></author><author><name sortKey="Aziz Alaoui, M A" sort="Aziz Alaoui, M A" uniqKey="Aziz Alaoui M" first="M A" last="Aziz-Alaoui">M A Aziz-Alaoui</name><affiliation wicri:level="3"><nlm:affiliation>UNIHAVRE, LMAH, FR-CNRS-3335, ISCN, Normandie University, 76600 Le Havre, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>UNIHAVRE, LMAH, FR-CNRS-3335, ISCN, Normandie University, 76600 Le Havre</wicri:regionArea><placeName><region type="region" nuts="2">Région Normandie</region><region type="old region" nuts="2">Haute-Normandie</region><settlement type="city">Le Havre</settlement></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32599867</idno><idno type="pmid">32599867</idno><idno type="doi">10.3390/biology9060135</idno><idno type="pmc">PMC7344619</idno><idno type="wicri:Area/Main/Corpus">000B70</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000B70</idno><idno type="wicri:Area/Main/Curation">000B70</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000B70</idno><idno type="wicri:Area/Main/Exploration">000B70</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">On a Coupled Time-Dependent SIR Models Fitting with New York and New-Jersey States COVID-19 Data.</title><author><name sortKey="Ambrosio, Benjamin" sort="Ambrosio, Benjamin" uniqKey="Ambrosio B" first="Benjamin" last="Ambrosio">Benjamin Ambrosio</name><affiliation wicri:level="3"><nlm:affiliation>UNIHAVRE, LMAH, FR-CNRS-3335, ISCN, Normandie University, 76600 Le Havre, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>UNIHAVRE, LMAH, FR-CNRS-3335, ISCN, Normandie University, 76600 Le Havre</wicri:regionArea><placeName><region type="region" nuts="2">Région Normandie</region><region type="old region" nuts="2">Haute-Normandie</region><settlement type="city">Le Havre</settlement></placeName></affiliation></author><author><name sortKey="Aziz Alaoui, M A" sort="Aziz Alaoui, M A" uniqKey="Aziz Alaoui M" first="M A" last="Aziz-Alaoui">M A Aziz-Alaoui</name><affiliation wicri:level="3"><nlm:affiliation>UNIHAVRE, LMAH, FR-CNRS-3335, ISCN, Normandie University, 76600 Le Havre, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>UNIHAVRE, LMAH, FR-CNRS-3335, ISCN, Normandie University, 76600 Le Havre</wicri:regionArea><placeName><region type="region" nuts="2">Région Normandie</region><region type="old region" nuts="2">Haute-Normandie</region><settlement type="city">Le Havre</settlement></placeName></affiliation></author></analytic><series><title level="j">Biology</title><idno type="ISSN">2079-7737</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 article describes a simple Susceptible Infected Recovered (SIR) model fitting with COVID-19 data for the month of March 2020 in New York (NY) state. The model is a classical SIR, but is non-autonomous; the rate of susceptible people becoming infected is adjusted over time in order to fit the available data. The death rate is also secondarily adjusted. Our fitting is made under the assumption that due to limiting number of tests, a large part of the infected population has not been tested positive. In the last part, we extend the model to take into account the daily fluxes between New Jersey (NJ) and NY states and fit the data for both states. Our simple model fits the available data, and illustrates typical dynamics of the disease: exponential increase, apex and decrease. The model highlights a decrease in the transmission rate over the period which gives a quantitative illustration about how lockdown policies reduce the spread of the pandemic. The coupled model with NY and NJ states shows a wave in NJ following the NY wave, illustrating the mechanism of spread from one attractive hot spot to its neighbor.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32599867</PMID><DateRevised><Year>2020</Year><Month>07</Month><Day>10</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Print">2079-7737</ISSN><JournalIssue CitedMedium="Print"><Volume>9</Volume><Issue>6</Issue><PubDate><Year>2020</Year><Month>Jun</Month><Day>24</Day></PubDate></JournalIssue><Title>Biology</Title><ISOAbbreviation>Biology (Basel)</ISOAbbreviation></Journal><ArticleTitle>On a Coupled Time-Dependent SIR Models Fitting with New York and New-Jersey States COVID-19 Data.</ArticleTitle><ELocationID EIdType="pii" ValidYN="Y">E135</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.3390/biology9060135</ELocationID><Abstract><AbstractText>This article describes a simple Susceptible Infected Recovered (SIR) model fitting with COVID-19 data for the month of March 2020 in New York (NY) state. The model is a classical SIR, but is non-autonomous; the rate of susceptible people becoming infected is adjusted over time in order to fit the available data. The death rate is also secondarily adjusted. Our fitting is made under the assumption that due to limiting number of tests, a large part of the infected population has not been tested positive. In the last part, we extend the model to take into account the daily fluxes between New Jersey (NJ) and NY states and fit the data for both states. Our simple model fits the available data, and illustrates typical dynamics of the disease: exponential increase, apex and decrease. The model highlights a decrease in the transmission rate over the period which gives a quantitative illustration about how lockdown policies reduce the spread of the pandemic. The coupled model with NY and NJ states shows a wave in NJ following the NY wave, illustrating the mechanism of spread from one attractive hot spot to its neighbor.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Ambrosio</LastName><ForeName>Benjamin</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>UNIHAVRE, LMAH, FR-CNRS-3335, ISCN, Normandie University, 76600 Le Havre, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Aziz-Alaoui</LastName><ForeName>M A</ForeName><Initials>MA</Initials><AffiliationInfo><Affiliation>UNIHAVRE, LMAH, FR-CNRS-3335, ISCN, Normandie University, 76600 Le Havre, France.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>06</Month><Day>24</Day></ArticleDate></Article><MedlineJournalInfo><Country>Switzerland</Country><MedlineTA>Biology (Basel)</MedlineTA><NlmUniqueID>101587988</NlmUniqueID><ISSNLinking>2079-7737</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">Network</Keyword><Keyword MajorTopicYN="N">New Jersey</Keyword><Keyword MajorTopicYN="N">New York</Keyword><Keyword MajorTopicYN="N">SIR models</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>05</Month><Day>14</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>06</Month><Day>05</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>06</Month><Day>19</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>7</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>7</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>7</Month><Day>1</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32599867</ArticleId><ArticleId IdType="pii">biology9060135</ArticleId><ArticleId IdType="doi">10.3390/biology9060135</ArticleId><ArticleId IdType="pmc">PMC7344619</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Math Biosci Eng. 2012 Apr;9(2):369-92</Citation><ArticleIdList><ArticleId IdType="pubmed">22901069</ArticleId></ArticleIdList></Reference><Reference><Citation>J Clin Med. 2020 Mar 13;9(3):</Citation><ArticleIdList><ArticleId IdType="pubmed">32183172</ArticleId></ArticleIdList></Reference><Reference><Citation>Philos Trans R Soc Lond B Biol Sci. 2019 Jul 8;374(1776):20180431</Citation><ArticleIdList><ArticleId IdType="pubmed">31104606</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet Infect Dis. 2020 Jul;20(7):773</Citation><ArticleIdList><ArticleId IdType="pubmed">32171390</ArticleId></ArticleIdList></Reference><Reference><Citation>J Math Biol. 1992;30(7):717-31</Citation><ArticleIdList><ArticleId IdType="pubmed">1522393</ArticleId></ArticleIdList></Reference><Reference><Citation>Int J Infect Dis. 2020 Apr;93:211-216</Citation><ArticleIdList><ArticleId IdType="pubmed">32145465</ArticleId></ArticleIdList></Reference><Reference><Citation>Biology (Basel). 2020 Mar 08;9(3):</Citation><ArticleIdList><ArticleId IdType="pubmed">32182724</ArticleId></ArticleIdList></Reference><Reference><Citation>Infect Dis Poverty. 2020 Feb 28;9(1):24</Citation><ArticleIdList><ArticleId IdType="pubmed">32111262</ArticleId></ArticleIdList></Reference><Reference><Citation>Math Biosci. 2011 Jan;229(1):50-63</Citation><ArticleIdList><ArticleId IdType="pubmed">21070789</ArticleId></ArticleIdList></Reference><Reference><Citation>Math Biosci Eng. 2018 Apr 1;15(2):441-460</Citation><ArticleIdList><ArticleId IdType="pubmed">29161844</ArticleId></ArticleIdList></Reference><Reference><Citation>J Clin Med. 2020 Feb 07;9(2):</Citation><ArticleIdList><ArticleId IdType="pubmed">32046137</ArticleId></ArticleIdList></Reference><Reference><Citation>Biology (Basel). 2020 May 08;9(5):</Citation><ArticleIdList><ArticleId IdType="pubmed">32397286</ArticleId></ArticleIdList></Reference><Reference><Citation>Infect Dis Clin North Am. 2019 Dec;33(4):869-889</Citation><ArticleIdList><ArticleId IdType="pubmed">31668196</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>France</li></country><region><li>Haute-Normandie</li><li>Région Normandie</li></region><settlement><li>Le Havre</li></settlement></list><tree><country name="France"><region name="Région Normandie"><name sortKey="Ambrosio, Benjamin" sort="Ambrosio, Benjamin" uniqKey="Ambrosio B" first="Benjamin" last="Ambrosio">Benjamin Ambrosio</name></region><name sortKey="Aziz Alaoui, M A" sort="Aziz Alaoui, M A" uniqKey="Aziz Alaoui M" first="M A" last="Aziz-Alaoui">M A Aziz-Alaoui</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/CovidFranceV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000A22 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000A22 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Sante
|area= CovidFranceV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= pubmed:32599867
|texte= On a Coupled Time-Dependent SIR Models Fitting with New York and New-Jersey States COVID-19 Data.
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Exploration/RBID.i -Sk "pubmed:32599867" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd \
| NlmPubMed2Wicri -a CovidFranceV1
| This area was generated with Dilib version V0.6.37. |  |