Temporal Variability and Social Heterogeneity in Disease Transmission: The Case of SARS in Hong Kong
Identifieur interne : 000235 ( France/Analysis ); précédent : 000234; suivant : 000236Temporal Variability and Social Heterogeneity in Disease Transmission: The Case of SARS in Hong Kong
Auteurs : Anne Cori [France] ; Pierre-Yves Boëlle [France] ; Guy Thomas [France] ; Gabriel M. Leung [République populaire de Chine] ; Alain-Jacques Valleron [France]Source :
- PLoS Computational Biology [ 1553-734X ] ; 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.
Url:
DOI: 10.1371/journal.pcbi.1000471
PubMed: 19696879
PubMed Central: 2717369
Affiliations:
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PMC:2717369Le document en format XML
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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"><p>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.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Morse, Ss" uniqKey="Morse S">SS Morse</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chan Yeung, M" uniqKey="Chan Yeung M">M Chan-Yeung</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lau, Jt" uniqKey="Lau J">JT Lau</name></author><author><name sortKey="Fung, Ks" uniqKey="Fung K">KS Fung</name></author><author><name sortKey="Wong, Tw" uniqKey="Wong T">TW Wong</name></author><author><name sortKey="Kim, Jh" uniqKey="Kim J">JH Kim</name></author><author><name sortKey="Wong, E" uniqKey="Wong E">E Wong</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Nishiura, H" 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Richardson</name></author><author><name sortKey="Spiegelhalter, D" uniqKey="Spiegelhalter D">D Spiegelhalter</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Robert, C" uniqKey="Robert C">C Robert</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Fraser, C" uniqKey="Fraser C">C Fraser</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gumel, Ab" uniqKey="Gumel A">AB Gumel</name></author><author><name sortKey="Ruan, S" uniqKey="Ruan S">S Ruan</name></author><author><name sortKey="Day, T" uniqKey="Day T">T Day</name></author><author><name sortKey="Watmough, J" uniqKey="Watmough J">J Watmough</name></author><author><name sortKey="Brauer, F" uniqKey="Brauer F">F Brauer</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Peiris, Js" uniqKey="Peiris J">JS Peiris</name></author><author><name sortKey="Chu, Cm" uniqKey="Chu C">CM Chu</name></author><author><name sortKey="Cheng, Vc" uniqKey="Cheng V">VC Cheng</name></author><author><name sortKey="Chan, Ks" uniqKey="Chan K">KS Chan</name></author><author><name sortKey="Hung, If" uniqKey="Hung I">IF Hung</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pitzer, Ve" uniqKey="Pitzer V">VE Pitzer</name></author><author><name sortKey="Leung, Gm" uniqKey="Leung G">GM Leung</name></author><author><name sortKey="Lipsitch, M" uniqKey="Lipsitch M">M Lipsitch</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Fox, Aj" uniqKey="Fox A">AJ Fox</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Li, Y" uniqKey="Li Y">Y Li</name></author><author><name sortKey="Yu, It" uniqKey="Yu I">IT Yu</name></author><author><name sortKey="Xu, P" uniqKey="Xu P">P Xu</name></author><author><name sortKey="Lee, Jh" uniqKey="Lee J">JH Lee</name></author><author><name sortKey="Wong, Tw" uniqKey="Wong T">TW Wong</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wong, Tw" uniqKey="Wong T">TW Wong</name></author><author><name sortKey="Lee, Ck" uniqKey="Lee C">CK Lee</name></author><author><name sortKey="Tam, W" uniqKey="Tam W">W Tam</name></author><author><name sortKey="Lau, Jt" uniqKey="Lau J">JT Lau</name></author><author><name sortKey="Yu, Ts" uniqKey="Yu T">TS Yu</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>France</li><li>République populaire de Chine</li></country><region><li>Île-de-France</li></region><settlement><li>Paris</li></settlement></list><tree><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><name sortKey="Boelle, Pierre Yves" sort="Boelle, Pierre Yves" uniqKey="Boelle P" first="Pierre-Yves" last="Boëlle">Pierre-Yves Boëlle</name><name sortKey="Boelle, Pierre Yves" sort="Boelle, Pierre Yves" uniqKey="Boelle P" first="Pierre-Yves" last="Boëlle">Pierre-Yves Boëlle</name><name sortKey="Cori, Anne" sort="Cori, Anne" uniqKey="Cori A" first="Anne" last="Cori">Anne Cori</name><name sortKey="Thomas, Guy" sort="Thomas, Guy" uniqKey="Thomas G" first="Guy" last="Thomas">Guy Thomas</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><name sortKey="Valleron, Alain Jacques" sort="Valleron, Alain Jacques" uniqKey="Valleron A" first="Alain-Jacques" last="Valleron">Alain-Jacques Valleron</name></country><country name="République populaire de Chine"><noRegion><name sortKey="Leung, Gabriel M" sort="Leung, Gabriel M" uniqKey="Leung G" first="Gabriel M." last="Leung">Gabriel M. Leung</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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