The Impact of Prior Information on Estimates of Disease Transmissibility Using Bayesian Tools
Identifieur interne : 001224 ( Main/Exploration ); précédent : 001223; suivant : 001225The Impact of Prior Information on Estimates of Disease Transmissibility Using Bayesian Tools
Auteurs : Carlee B. Moser [États-Unis] ; Mayetri Gupta [États-Unis] ; Brett N. Archer [Afrique du Sud] ; Laura F. White [États-Unis]Source :
- PLoS ONE [ 1932-6203 ] ; 2015.
Descripteurs français
- KwdFr :
- Flambées de maladies (), Grippe humaine (transmission), Grippe humaine (virologie), Grippe humaine (épidémiologie), Hong Kong (épidémiologie), Humains, Intervalles de confiance, République d'Afrique du Sud (épidémiologie), Simulation numérique, Singapour (épidémiologie), Sous-type H1N1 du virus de la grippe A, Syndrome respiratoire aigu sévère (épidémiologie), Théorème de Bayes, Traçage des contacts.
- MESH :
- virologie : Grippe humaine.
- épidémiologie : Grippe humaine, Hong Kong, République d'Afrique du Sud, Singapour, Syndrome respiratoire aigu sévère.
- Flambées de maladies, Humains, Intervalles de confiance, Simulation numérique, Sous-type H1N1 du virus de la grippe A, Théorème de Bayes, Traçage des contacts.
- Wicri :
- geographic : Hong Kong, Singapour, Afrique du Sud.
English descriptors
- KwdEn :
- Bayes Theorem, Computer Simulation, Confidence Intervals, Contact Tracing, Disease Outbreaks (statistics & numerical data), Hong Kong (epidemiology), Humans, Influenza A Virus, H1N1 Subtype, Influenza, Human (epidemiology), Influenza, Human (transmission), Influenza, Human (virology), Severe Acute Respiratory Syndrome (epidemiology), Singapore (epidemiology), South Africa (epidemiology).
- MESH :
- geographic , epidemiology : Hong Kong, Singapore, South Africa.
- epidemiology : Influenza, Human, Severe Acute Respiratory Syndrome.
- statistics & numerical data : Disease Outbreaks.
- transmission : Influenza, Human.
- virology : Influenza, Human.
- Bayes Theorem, Computer Simulation, Confidence Intervals, Contact Tracing, Humans, Influenza A Virus, H1N1 Subtype.
Abstract
The basic reproductive number (R₀) and the distribution of the serial interval (SI) are often used to quantify transmission during an infectious disease outbreak. In this paper, we present estimates of R₀ and SI from the 2003 SARS outbreak in Hong Kong and Singapore, and the 2009 pandemic influenza A(H1N1) outbreak in South Africa using methods that expand upon an existing Bayesian framework. This expanded framework allows for the incorporation of additional information, such as contact tracing or household data, through prior distributions. The results for the R₀ and the SI from the influenza outbreak in South Africa were similar regardless of the prior information (
Url:
DOI: 10.1371/journal.pone.0118762
PubMed: 25793993
PubMed Central: 4368801
Affiliations:
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Moser</name><affiliation wicri:level="2"><nlm:aff id="aff001"><addr-line>Department of Biostatistics, Boston University School of Public Health, Boston University, Boston, Massachusetts, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biostatistics, Boston University School of Public Health, Boston University, Boston, Massachusetts</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation></author><author><name sortKey="Gupta, Mayetri" sort="Gupta, Mayetri" uniqKey="Gupta M" first="Mayetri" last="Gupta">Mayetri Gupta</name><affiliation wicri:level="2"><nlm:aff id="aff001"><addr-line>Department of Biostatistics, Boston University School of Public Health, Boston University, Boston, Massachusetts, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biostatistics, Boston University School of Public Health, Boston University, Boston, Massachusetts</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation></author><author><name sortKey="Archer, Brett N" sort="Archer, Brett N" uniqKey="Archer B" first="Brett N." last="Archer">Brett N. Archer</name><affiliation wicri:level="1"><nlm:aff id="aff002"><addr-line>National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa</addr-line></nlm:aff><country xml:lang="fr">Afrique du Sud</country><wicri:regionArea>National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg</wicri:regionArea><wicri:noRegion>Johannesburg</wicri:noRegion></affiliation></author><author><name sortKey="White, Laura F" sort="White, Laura F" uniqKey="White L" first="Laura F." last="White">Laura F. White</name><affiliation wicri:level="2"><nlm:aff id="aff001"><addr-line>Department of Biostatistics, Boston University School of Public Health, Boston University, Boston, Massachusetts, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biostatistics, Boston University School of Public Health, Boston University, Boston, Massachusetts</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Bayes Theorem</term><term>Computer Simulation</term><term>Confidence Intervals</term><term>Contact Tracing</term><term>Disease Outbreaks (statistics & numerical data)</term><term>Hong Kong (epidemiology)</term><term>Humans</term><term>Influenza A Virus, H1N1 Subtype</term><term>Influenza, Human (epidemiology)</term><term>Influenza, Human (transmission)</term><term>Influenza, Human (virology)</term><term>Severe Acute Respiratory Syndrome (epidemiology)</term><term>Singapore (epidemiology)</term><term>South Africa (epidemiology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Flambées de maladies ()</term><term>Grippe humaine (transmission)</term><term>Grippe humaine (virologie)</term><term>Grippe humaine (épidémiologie)</term><term>Hong Kong (épidémiologie)</term><term>Humains</term><term>Intervalles de confiance</term><term>République d'Afrique du Sud (épidémiologie)</term><term>Simulation numérique</term><term>Singapour (épidémiologie)</term><term>Sous-type H1N1 du virus de la grippe A</term><term>Syndrome respiratoire aigu sévère (épidémiologie)</term><term>Théorème de Bayes</term><term>Traçage des contacts</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Hong Kong</term><term>Singapore</term><term>South Africa</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term><term>Severe Acute Respiratory Syndrome</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Disease Outbreaks</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="virologie" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term><term>Hong Kong</term><term>République d'Afrique du Sud</term><term>Singapour</term><term>Syndrome respiratoire aigu sévère</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Bayes Theorem</term><term>Computer Simulation</term><term>Confidence Intervals</term><term>Contact Tracing</term><term>Humans</term><term>Influenza A Virus, H1N1 Subtype</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Flambées de maladies</term><term>Humains</term><term>Intervalles de confiance</term><term>Simulation numérique</term><term>Sous-type H1N1 du virus de la grippe A</term><term>Théorème de Bayes</term><term>Traçage des contacts</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Hong Kong</term><term>Singapour</term><term>Afrique du Sud</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>The basic reproductive number (R₀) and the distribution of the serial interval (SI) are often used to quantify transmission during an infectious disease outbreak. In this paper, we present estimates of R₀ and SI from the 2003 SARS outbreak in Hong Kong and Singapore, and the 2009 pandemic influenza A(H1N1) outbreak in South Africa using methods that expand upon an existing Bayesian framework. This expanded framework allows for the incorporation of additional information, such as contact tracing or household data, through prior distributions. The results for the R₀ and the SI from the influenza outbreak in South Africa were similar regardless of the prior information (<inline-formula id="pone.0118762.e001"><alternatives><graphic xlink:href="pone.0118762.e001.jpg" id="pone.0118762.e001g" position="anchor" mimetype="image" orientation="portrait"></graphic><mml:math id="M1"><mml:msub><mml:mrow><mml:mover accent="true"><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mo>^</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math></alternatives></inline-formula> = 1.36–1.46, <inline-formula id="pone.0118762.e002"><alternatives><graphic xlink:href="pone.0118762.e002.jpg" id="pone.0118762.e002g" position="anchor" mimetype="image" orientation="portrait"></graphic><mml:math id="M2"><mml:mrow><mml:mover accent="true"><mml:mrow><mml:mi>μ</mml:mi></mml:mrow><mml:mo>^</mml:mo></mml:mover></mml:mrow></mml:math></alternatives></inline-formula> = 2.0–2.7, <inline-formula id="pone.0118762.e003"><alternatives><graphic xlink:href="pone.0118762.e003.jpg" id="pone.0118762.e003g" position="anchor" mimetype="image" orientation="portrait"></graphic><mml:math id="M3"><mml:mrow><mml:mover accent="true"><mml:mrow><mml:mi>μ</mml:mi></mml:mrow><mml:mo>^</mml:mo></mml:mover></mml:mrow></mml:math></alternatives></inline-formula> = mean of the SI). The estimates of R₀ and μ for the SARS outbreak ranged from 2.0–4.4 and 7.4–11.3, respectively, and were shown to vary depending on the use of contact tracing data. The impact of the contact tracing data was likely due to the small number of SARS cases relative to the size of the contact tracing sample.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Anderson, Rm" uniqKey="Anderson R">RM Anderson</name></author><author><name sortKey="May, M" uniqKey="May M">M May</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Svensson, A" uniqKey="Svensson A">A Svensson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Fraser, C" uniqKey="Fraser C">C Fraser</name></author><author><name sortKey="Riley, S" uniqKey="Riley S">S Riley</name></author><author><name sortKey="Anderson, R" uniqKey="Anderson R">R Anderson</name></author><author><name sortKey="Ferguson, N" uniqKey="Ferguson N">N Ferguson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cowling, Bj" uniqKey="Cowling B">BJ 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Bergeri</name></author><author><name sortKey="Van Gemert, C" uniqKey="Van Gemert C">C van Gemert</name></author><author><name sortKey="Rotty, J" uniqKey="Rotty J">J Rotty</name></author><author><name sortKey="Headley, Ej" uniqKey="Headley E">EJ Headley</name></author><author><name sortKey="Simpson, K" uniqKey="Simpson K">K Simpson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wallinga, J" uniqKey="Wallinga J">J Wallinga</name></author><author><name sortKey="Teunis, P" uniqKey="Teunis P">P Teunis</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lipsitch, M" uniqKey="Lipsitch M">M Lipsitch</name></author><author><name sortKey="Cohen, T" uniqKey="Cohen T">T Cohen</name></author><author><name sortKey="Cooper, B" uniqKey="Cooper B">B Cooper</name></author><author><name sortKey="Robins, J" uniqKey="Robins J">J Robins</name></author><author><name sortKey="Ma, S" uniqKey="Ma S">S Ma</name></author><author><name 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Griffin</name></author><author><name sortKey="Garske, T" uniqKey="Garske T">T Garske</name></author><author><name sortKey="Ghani, A" uniqKey="Ghani A">A Ghani</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hens, N" uniqKey="Hens N">N Hens</name></author><author><name sortKey="Calatayud, L" uniqKey="Calatayud L">L Calatayud</name></author><author><name sortKey="Kurkela, S" uniqKey="Kurkela S">S Kurkela</name></author><author><name sortKey="Tamme, T" uniqKey="Tamme T">T Tamme</name></author><author><name sortKey="Wallinga, J" uniqKey="Wallinga J">J Wallinga</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Te Beest, De" uniqKey="Te Beest D">DE te Beest</name></author><author><name sortKey="Wallinga, J" uniqKey="Wallinga J">J Wallinga</name></author><author><name sortKey="Donker, T" uniqKey="Donker T">T Donker</name></author><author><name sortKey="Van Boven, M" uniqKey="Van Boven M">M van Boven</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Afrique du Sud</li><li>États-Unis</li></country><region><li>Massachusetts</li></region></list><tree><country name="États-Unis"><region name="Massachusetts"><name sortKey="Moser, Carlee B" sort="Moser, Carlee B" uniqKey="Moser C" first="Carlee B." last="Moser">Carlee B. Moser</name></region><name sortKey="Gupta, Mayetri" sort="Gupta, Mayetri" uniqKey="Gupta M" first="Mayetri" last="Gupta">Mayetri Gupta</name><name sortKey="White, Laura F" sort="White, Laura F" uniqKey="White L" first="Laura F." last="White">Laura F. White</name></country><country name="Afrique du Sud"><noRegion><name sortKey="Archer, Brett N" sort="Archer, Brett N" uniqKey="Archer B" first="Brett N." last="Archer">Brett N. Archer</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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