Temporal variability and social heterogeneity in disease transmission: the case of SARS in Hong Kong.
Identifieur interne : 001768 ( PubMed/Checkpoint ); précédent : 001767; suivant : 001769Temporal variability and social heterogeneity in disease transmission: the case of SARS in Hong Kong.
Auteurs : Anne Cori [France] ; Pierre-Yves Boëlle ; Guy Thomas ; Gabriel M. Leung ; Alain-Jacques ValleronSource :
- PLoS computational biology [ 1553-7358 ] ; 2009.
Descripteurs français
- KwdFr :
- Algorithmes, Chaines de Markov, Facteurs temps, Flambées de maladies, Hong Kong (épidémiologie), Humains, Infection croisée (transmission), Infection croisée (virologie), Infection croisée (épidémiologie), Infections communautaires (transmission), Infections communautaires (virologie), Infections communautaires (épidémiologie), Modèles statistiques, Méthode de Monte-Carlo, Personnel de santé, Processus stochastiques, Syndrome respiratoire aigu sévère (transmission), Syndrome respiratoire aigu sévère (épidémiologie), Transmission de maladie infectieuse du patient au professionnel de santé, Transmission de maladie infectieuse du professionnel de santé au patient, Virus du SRAS.
- MESH :
- virologie : Infection croisée, Infections communautaires.
- épidémiologie : Hong Kong, Infection croisée, Infections communautaires, Syndrome respiratoire aigu sévère.
- Algorithmes, Chaines de Markov, Facteurs temps, Flambées de maladies, Humains, Modèles statistiques, Méthode de Monte-Carlo, Personnel de santé, Processus stochastiques, Transmission de maladie infectieuse du patient au professionnel de santé, Transmission de maladie infectieuse du professionnel de santé au patient, Virus du SRAS.
- Wicri :
- geographic : Hong Kong.
English descriptors
- KwdEn :
- Algorithms, Community-Acquired Infections (epidemiology), Community-Acquired Infections (transmission), Community-Acquired Infections (virology), Cross Infection (epidemiology), Cross Infection (transmission), Cross Infection (virology), Disease Outbreaks, Health Personnel, Hong Kong (epidemiology), Humans, Infectious Disease Transmission, Patient-to-Professional, Infectious Disease Transmission, Professional-to-Patient, Markov Chains, Models, Statistical, Monte Carlo Method, SARS Virus, Severe Acute Respiratory Syndrome (epidemiology), Severe Acute Respiratory Syndrome (transmission), Stochastic Processes, Time Factors.
- MESH :
- geographic , epidemiology : Hong Kong.
- epidemiology : Community-Acquired Infections, Cross Infection, Severe Acute Respiratory Syndrome.
- transmission : Community-Acquired Infections, Cross Infection, Severe Acute Respiratory Syndrome.
- virology : Community-Acquired Infections, Cross Infection.
- Algorithms, Disease Outbreaks, Health Personnel, Humans, Infectious Disease Transmission, Patient-to-Professional, Infectious Disease Transmission, Professional-to-Patient, Markov Chains, Models, Statistical, Monte Carlo Method, SARS Virus, Stochastic Processes, Time Factors.
Abstract
The extent to which self-adopted or intervention-related changes in behaviors affect the course of epidemics remains a key issue for outbreak control. This study attempted to quantify the effect of such changes on the risk of infection in different settings, i.e., the community and hospitals. The 2002-2003 severe acute respiratory syndrome (SARS) outbreak in Hong Kong, where 27% of cases were healthcare workers, was used as an example. A stochastic compartmental SEIR (susceptible-exposed-infectious-removed) model was used: the population was split into healthcare workers, hospitalized people and general population. Super spreading events (SSEs) were taken into account in the model. The temporal evolutions of the daily effective contact rates in the community and hospitals were modeled with smooth functions. Data augmentation techniques and Markov chain Monte Carlo (MCMC) methods were applied to estimate SARS epidemiological parameters. In particular, estimates of daily reproduction numbers were provided for each subpopulation. The average duration of the SARS infectious period was estimated to be 9.3 days (+/-0.3 days). The model was able to disentangle the impact of the two SSEs from background transmission rates. The effective contact rates, which were estimated on a daily basis, decreased with time, reaching zero inside hospitals. This observation suggests that public health measures and possible changes in individual behaviors effectively reduced transmission, especially in hospitals. The temporal patterns of reproduction numbers were similar for healthcare workers and the general population, indicating that on average, an infectious healthcare worker did not infect more people than any other infectious person. We provide a general method to estimate time dependence of parameters in structured epidemic models, which enables investigation of the impact of control measures and behavioral changes in different settings.
DOI: 10.1371/journal.pcbi.1000471
PubMed: 19696879
Affiliations:
Links toward previous steps (curation, corpus...)
Links to Exploration step
pubmed:19696879Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Temporal variability and social heterogeneity in disease transmission: the case of SARS in Hong Kong.</title><author><name sortKey="Cori, Anne" sort="Cori, Anne" uniqKey="Cori A" first="Anne" last="Cori">Anne Cori</name><affiliation wicri:level="3"><nlm:affiliation>INSERM, Paris, France. cori@u707.jussieu.fr</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>INSERM, Paris</wicri:regionArea><placeName><region type="region">Île-de-France</region><region type="old region">Île-de-France</region><settlement type="city">Paris</settlement></placeName></affiliation></author><author><name sortKey="Boelle, Pierre Yves" sort="Boelle, Pierre Yves" uniqKey="Boelle P" first="Pierre-Yves" last="Boëlle">Pierre-Yves Boëlle</name></author><author><name sortKey="Thomas, Guy" sort="Thomas, Guy" uniqKey="Thomas G" first="Guy" last="Thomas">Guy Thomas</name></author><author><name sortKey="Leung, Gabriel M" sort="Leung, Gabriel M" uniqKey="Leung G" first="Gabriel M" last="Leung">Gabriel M. Leung</name></author><author><name sortKey="Valleron, Alain Jacques" sort="Valleron, Alain Jacques" uniqKey="Valleron A" first="Alain-Jacques" last="Valleron">Alain-Jacques Valleron</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2009">2009</date><idno type="RBID">pubmed:19696879</idno><idno type="pmid">19696879</idno><idno type="doi">10.1371/journal.pcbi.1000471</idno><idno type="wicri:Area/PubMed/Corpus">001843</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001843</idno><idno type="wicri:Area/PubMed/Curation">001843</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">001843</idno><idno type="wicri:Area/PubMed/Checkpoint">001768</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">001768</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Temporal variability and social heterogeneity in disease transmission: the case of SARS in Hong Kong.</title><author><name sortKey="Cori, Anne" sort="Cori, Anne" uniqKey="Cori A" first="Anne" last="Cori">Anne Cori</name><affiliation wicri:level="3"><nlm:affiliation>INSERM, Paris, France. cori@u707.jussieu.fr</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>INSERM, Paris</wicri:regionArea><placeName><region type="region">Île-de-France</region><region type="old region">Île-de-France</region><settlement type="city">Paris</settlement></placeName></affiliation></author><author><name sortKey="Boelle, Pierre Yves" sort="Boelle, Pierre Yves" uniqKey="Boelle P" first="Pierre-Yves" last="Boëlle">Pierre-Yves Boëlle</name></author><author><name sortKey="Thomas, Guy" sort="Thomas, Guy" uniqKey="Thomas G" first="Guy" last="Thomas">Guy Thomas</name></author><author><name sortKey="Leung, Gabriel M" sort="Leung, Gabriel M" uniqKey="Leung G" first="Gabriel M" last="Leung">Gabriel M. Leung</name></author><author><name sortKey="Valleron, Alain Jacques" sort="Valleron, Alain Jacques" uniqKey="Valleron A" first="Alain-Jacques" last="Valleron">Alain-Jacques Valleron</name></author></analytic><series><title level="j">PLoS computational biology</title><idno type="eISSN">1553-7358</idno><imprint><date when="2009" type="published">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Community-Acquired Infections (epidemiology)</term><term>Community-Acquired Infections (transmission)</term><term>Community-Acquired Infections (virology)</term><term>Cross Infection (epidemiology)</term><term>Cross Infection (transmission)</term><term>Cross Infection (virology)</term><term>Disease Outbreaks</term><term>Health Personnel</term><term>Hong Kong (epidemiology)</term><term>Humans</term><term>Infectious Disease Transmission, Patient-to-Professional</term><term>Infectious Disease Transmission, Professional-to-Patient</term><term>Markov Chains</term><term>Models, Statistical</term><term>Monte Carlo Method</term><term>SARS Virus</term><term>Severe Acute Respiratory Syndrome (epidemiology)</term><term>Severe Acute Respiratory Syndrome (transmission)</term><term>Stochastic Processes</term><term>Time Factors</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Chaines de Markov</term><term>Facteurs temps</term><term>Flambées de maladies</term><term>Hong Kong (épidémiologie)</term><term>Humains</term><term>Infection croisée (transmission)</term><term>Infection croisée (virologie)</term><term>Infection croisée (épidémiologie)</term><term>Infections communautaires (transmission)</term><term>Infections communautaires (virologie)</term><term>Infections communautaires (épidémiologie)</term><term>Modèles statistiques</term><term>Méthode de Monte-Carlo</term><term>Personnel de santé</term><term>Processus stochastiques</term><term>Syndrome respiratoire aigu sévère (transmission)</term><term>Syndrome respiratoire aigu sévère (épidémiologie)</term><term>Transmission de maladie infectieuse du patient au professionnel de santé</term><term>Transmission de maladie infectieuse du professionnel de santé au patient</term><term>Virus du SRAS</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Hong Kong</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Community-Acquired Infections</term><term>Cross Infection</term><term>Severe Acute Respiratory Syndrome</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Community-Acquired Infections</term><term>Cross Infection</term><term>Severe Acute Respiratory Syndrome</term></keywords><keywords scheme="MESH" qualifier="virologie" xml:lang="fr"><term>Infection croisée</term><term>Infections communautaires</term></keywords><keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Community-Acquired Infections</term><term>Cross Infection</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Hong Kong</term><term>Infection croisée</term><term>Infections communautaires</term><term>Syndrome respiratoire aigu sévère</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Disease Outbreaks</term><term>Health Personnel</term><term>Humans</term><term>Infectious Disease Transmission, Patient-to-Professional</term><term>Infectious Disease Transmission, Professional-to-Patient</term><term>Markov Chains</term><term>Models, Statistical</term><term>Monte Carlo Method</term><term>SARS Virus</term><term>Stochastic Processes</term><term>Time Factors</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Chaines de Markov</term><term>Facteurs temps</term><term>Flambées de maladies</term><term>Humains</term><term>Modèles statistiques</term><term>Méthode de Monte-Carlo</term><term>Personnel de santé</term><term>Processus stochastiques</term><term>Transmission de maladie infectieuse du patient au professionnel de santé</term><term>Transmission de maladie infectieuse du professionnel de santé au patient</term><term>Virus du SRAS</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Hong Kong</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The extent to which self-adopted or intervention-related changes in behaviors affect the course of epidemics remains a key issue for outbreak control. This study attempted to quantify the effect of such changes on the risk of infection in different settings, i.e., the community and hospitals. The 2002-2003 severe acute respiratory syndrome (SARS) outbreak in Hong Kong, where 27% of cases were healthcare workers, was used as an example. A stochastic compartmental SEIR (susceptible-exposed-infectious-removed) model was used: the population was split into healthcare workers, hospitalized people and general population. Super spreading events (SSEs) were taken into account in the model. The temporal evolutions of the daily effective contact rates in the community and hospitals were modeled with smooth functions. Data augmentation techniques and Markov chain Monte Carlo (MCMC) methods were applied to estimate SARS epidemiological parameters. In particular, estimates of daily reproduction numbers were provided for each subpopulation. The average duration of the SARS infectious period was estimated to be 9.3 days (+/-0.3 days). The model was able to disentangle the impact of the two SSEs from background transmission rates. The effective contact rates, which were estimated on a daily basis, decreased with time, reaching zero inside hospitals. This observation suggests that public health measures and possible changes in individual behaviors effectively reduced transmission, especially in hospitals. The temporal patterns of reproduction numbers were similar for healthcare workers and the general population, indicating that on average, an infectious healthcare worker did not infect more people than any other infectious person. We provide a general method to estimate time dependence of parameters in structured epidemic models, which enables investigation of the impact of control measures and behavioral changes in different settings.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">19696879</PMID><DateCompleted><Year>2009</Year><Month>12</Month><Day>02</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1553-7358</ISSN><JournalIssue CitedMedium="Internet"><Volume>5</Volume><Issue>8</Issue><PubDate><Year>2009</Year><Month>Aug</Month></PubDate></JournalIssue><Title>PLoS computational biology</Title><ISOAbbreviation>PLoS Comput. Biol.</ISOAbbreviation></Journal><ArticleTitle>Temporal variability and social heterogeneity in disease transmission: the case of SARS in Hong Kong.</ArticleTitle><Pagination><MedlinePgn>e1000471</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pcbi.1000471</ELocationID><Abstract><AbstractText>The extent to which self-adopted or intervention-related changes in behaviors affect the course of epidemics remains a key issue for outbreak control. This study attempted to quantify the effect of such changes on the risk of infection in different settings, i.e., the community and hospitals. The 2002-2003 severe acute respiratory syndrome (SARS) outbreak in Hong Kong, where 27% of cases were healthcare workers, was used as an example. A stochastic compartmental SEIR (susceptible-exposed-infectious-removed) model was used: the population was split into healthcare workers, hospitalized people and general population. Super spreading events (SSEs) were taken into account in the model. The temporal evolutions of the daily effective contact rates in the community and hospitals were modeled with smooth functions. Data augmentation techniques and Markov chain Monte Carlo (MCMC) methods were applied to estimate SARS epidemiological parameters. In particular, estimates of daily reproduction numbers were provided for each subpopulation. The average duration of the SARS infectious period was estimated to be 9.3 days (+/-0.3 days). The model was able to disentangle the impact of the two SSEs from background transmission rates. The effective contact rates, which were estimated on a daily basis, decreased with time, reaching zero inside hospitals. This observation suggests that public health measures and possible changes in individual behaviors effectively reduced transmission, especially in hospitals. The temporal patterns of reproduction numbers were similar for healthcare workers and the general population, indicating that on average, an infectious healthcare worker did not infect more people than any other infectious person. We provide a general method to estimate time dependence of parameters in structured epidemic models, which enables investigation of the impact of control measures and behavioral changes in different settings.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Cori</LastName><ForeName>Anne</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>INSERM, Paris, France. cori@u707.jussieu.fr</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Boëlle</LastName><ForeName>Pierre-Yves</ForeName><Initials>PY</Initials></Author><Author ValidYN="Y"><LastName>Thomas</LastName><ForeName>Guy</ForeName><Initials>G</Initials></Author><Author ValidYN="Y"><LastName>Leung</LastName><ForeName>Gabriel M</ForeName><Initials>GM</Initials></Author><Author ValidYN="Y"><LastName>Valleron</LastName><ForeName>Alain-Jacques</ForeName><Initials>AJ</Initials></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>2009</Year><Month>08</Month><Day>21</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS Comput Biol</MedlineTA><NlmUniqueID>101238922</NlmUniqueID><ISSNLinking>1553-734X</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017714" MajorTopicYN="N">Community-Acquired Infections</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000635" MajorTopicYN="Y">transmission</QualifierName><QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003428" MajorTopicYN="N">Cross Infection</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000635" MajorTopicYN="Y">transmission</QualifierName><QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D004196" MajorTopicYN="Y">Disease Outbreaks</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006282" MajorTopicYN="N">Health Personnel</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006723" MajorTopicYN="N" Type="Geographic">Hong Kong</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017758" MajorTopicYN="N">Infectious Disease Transmission, Patient-to-Professional</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017757" MajorTopicYN="N">Infectious Disease Transmission, Professional-to-Patient</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008390" MajorTopicYN="N">Markov Chains</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015233" MajorTopicYN="Y">Models, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009010" MajorTopicYN="N">Monte Carlo Method</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D045473" MajorTopicYN="Y">SARS Virus</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D045169" MajorTopicYN="N">Severe Acute Respiratory Syndrome</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000635" MajorTopicYN="Y">transmission</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D013269" MajorTopicYN="N">Stochastic Processes</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013997" MajorTopicYN="N">Time Factors</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2008</Year><Month>10</Month><Day>13</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2009</Year><Month>07</Month><Day>15</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2009</Year><Month>8</Month><Day>22</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2009</Year><Month>8</Month><Day>22</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2009</Year><Month>12</Month><Day>16</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">19696879</ArticleId><ArticleId IdType="doi">10.1371/journal.pcbi.1000471</ArticleId><ArticleId IdType="pmc">PMC2717369</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>J Theor Biol. 2003 Sep 7;224(1):1-8</Citation><ArticleIdList><ArticleId IdType="pubmed">12900200</ArticleId></ArticleIdList></Reference><Reference><Citation>Science. 2003 Jun 20;300(5627):1966-70</Citation><ArticleIdList><ArticleId IdType="pubmed">12766207</ArticleId></ArticleIdList></Reference><Reference><Citation>Proc Biol Sci. 2003 Oct 7;270(1528):1979-89</Citation><ArticleIdList><ArticleId IdType="pubmed">14561285</ArticleId></ArticleIdList></Reference><Reference><Citation>Emerg Infect Dis. 2004 Feb;10(2):269-76</Citation><ArticleIdList><ArticleId IdType="pubmed">15030696</ArticleId></ArticleIdList></Reference><Reference><Citation>Emerg Infect Dis. 2004 Feb;10(2):280-6</Citation><ArticleIdList><ArticleId IdType="pubmed">15030698</ArticleId></ArticleIdList></Reference><Reference><Citation>Philos Trans R Soc Lond B Biol Sci. 2004 Jul 29;359(1447):1091-105</Citation><ArticleIdList><ArticleId IdType="pubmed">15306395</ArticleId></ArticleIdList></Reference><Reference><Citation>Am J Epidemiol. 2004 Oct 15;160(8):719-28</Citation><ArticleIdList><ArticleId IdType="pubmed">15466494</ArticleId></ArticleIdList></Reference><Reference><Citation>Emerg Infect Dis. 1995 Jan-Mar;1(1):7-15</Citation><ArticleIdList><ArticleId IdType="pubmed">8903148</ArticleId></ArticleIdList></Reference><Reference><Citation>Ann Intern Med. 2004 Nov 2;141(9):662-73</Citation><ArticleIdList><ArticleId IdType="pubmed">15520422</ArticleId></ArticleIdList></Reference><Reference><Citation>Proc Biol Sci. 2004 Nov 7;271(1554):2223-32</Citation><ArticleIdList><ArticleId IdType="pubmed">15539347</ArticleId></ArticleIdList></Reference><Reference><Citation>Int J Occup Environ Health. 2004 Oct-Dec;10(4):421-7</Citation><ArticleIdList><ArticleId IdType="pubmed">15702757</ArticleId></ArticleIdList></Reference><Reference><Citation>Am J Trop Med Hyg. 2005 Jul;73(1):17-25</Citation><ArticleIdList><ArticleId IdType="pubmed">16014825</ArticleId></ArticleIdList></Reference><Reference><Citation>Nature. 2005 Nov 17;438(7066):355-9</Citation><ArticleIdList><ArticleId IdType="pubmed">16292310</ArticleId></ArticleIdList></Reference><Reference><Citation>Emerg Infect Dis. 2006 Jan;12(1):110-3</Citation><ArticleIdList><ArticleId IdType="pubmed">16494726</ArticleId></ArticleIdList></Reference><Reference><Citation>Proc Biol Sci. 2007 Mar 7;274(1610):611-7</Citation><ArticleIdList><ArticleId IdType="pubmed">17254984</ArticleId></ArticleIdList></Reference><Reference><Citation>Am J Epidemiol. 2007 Aug 1;166(3):355-63</Citation><ArticleIdList><ArticleId IdType="pubmed">17493952</ArticleId></ArticleIdList></Reference><Reference><Citation>PLoS One. 2007;2(8):e758</Citation><ArticleIdList><ArticleId IdType="pubmed">17712406</ArticleId></ArticleIdList></Reference><Reference><Citation>N Engl J Med. 2003 May 15;348(20):1986-94</Citation><ArticleIdList><ArticleId IdType="pubmed">12682352</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet. 2003 May 24;361(9371):1767-72</Citation><ArticleIdList><ArticleId IdType="pubmed">12781535</ArticleId></ArticleIdList></Reference><Reference><Citation>Science. 2003 Jun 20;300(5627):1961-6</Citation><ArticleIdList><ArticleId IdType="pubmed">12766206</ArticleId></ArticleIdList></Reference><Reference><Citation>CMAJ. 2003 Aug 19;169(4):285-92</Citation><ArticleIdList><ArticleId IdType="pubmed">12925421</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>France</li></country><region><li>Île-de-France</li></region><settlement><li>Paris</li></settlement></list><tree><noCountry><name sortKey="Boelle, Pierre Yves" sort="Boelle, Pierre Yves" uniqKey="Boelle P" first="Pierre-Yves" last="Boëlle">Pierre-Yves Boëlle</name><name sortKey="Leung, Gabriel M" sort="Leung, Gabriel M" uniqKey="Leung G" first="Gabriel M" last="Leung">Gabriel M. Leung</name><name sortKey="Thomas, Guy" sort="Thomas, Guy" uniqKey="Thomas G" first="Guy" last="Thomas">Guy Thomas</name><name sortKey="Valleron, Alain Jacques" sort="Valleron, Alain Jacques" uniqKey="Valleron A" first="Alain-Jacques" last="Valleron">Alain-Jacques Valleron</name></noCountry><country name="France"><region name="Île-de-France"><name sortKey="Cori, Anne" sort="Cori, Anne" uniqKey="Cori A" first="Anne" last="Cori">Anne Cori</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/SrasV1/Data/PubMed/Checkpoint
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001768 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/PubMed/Checkpoint/biblio.hfd -nk 001768 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Sante
|area= SrasV1
|flux= PubMed
|étape= Checkpoint
|type= RBID
|clé= pubmed:19696879
|texte= Temporal variability and social heterogeneity in disease transmission: the case of SARS in Hong Kong.
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/PubMed/Checkpoint/RBID.i -Sk "pubmed:19696879" \
| HfdSelect -Kh $EXPLOR_AREA/Data/PubMed/Checkpoint/biblio.hfd \
| NlmPubMed2Wicri -a SrasV1
| This area was generated with Dilib version V0.6.33. |  |