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Article: Temporal variability and social heterogeneity in disease transmission: The case of SARS in Hong Kong

TitleTemporal variability and social heterogeneity in disease transmission: The case of SARS in Hong Kong
Authors
Issue Date2009
PublisherPublic Library of Science. The Journal's web site is located at http://www.ploscompbiol.org/
Citation
Plos Computational Biology, 2009, v. 5 n. 8 How to Cite?
AbstractThe 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. © 2009 Cori et al.
Persistent Identifierhttp://hdl.handle.net/10722/86777
ISSN
2015 Impact Factor: 4.587
2015 SCImago Journal Rankings: 3.405
PubMed Central ID
ISI Accession Number ID
Funding AgencyGrant Number
Food and Health Bureau of the Hong Kong SAR Government
University of Hong Kong SARS Research Fund
EUSP22-CT-2004-511066
FP6-2003-SSP2-513715
Funding Information:

The authors thank the following for research funding: The Research Fund for the Control of Infectious Diseases of the Food and Health Bureau of the Hong Kong SAR Government (GML); The University of Hong Kong SARS Research Fund (GML); The EU Sixth Framework Programme for Research for Policy Support (contracts SP22-CT-2004-511066 and FP6-2003-SSP2-513715) (AC, P-YB, GT,GML, A-JV). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

 

DC FieldValueLanguage
dc.contributor.authorCori, Aen_HK
dc.contributor.authorBoëlle, PYen_HK
dc.contributor.authorThomas, Gen_HK
dc.contributor.authorLeung, GMen_HK
dc.contributor.authorValleron, AJen_HK
dc.date.accessioned2010-09-06T09:21:11Z-
dc.date.available2010-09-06T09:21:11Z-
dc.date.issued2009en_HK
dc.identifier.citationPlos Computational Biology, 2009, v. 5 n. 8en_HK
dc.identifier.issn1553-734Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/86777-
dc.description.abstractThe 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. © 2009 Cori et al.en_HK
dc.languageengen_HK
dc.publisherPublic Library of Science. The Journal's web site is located at http://www.ploscompbiol.org/en_HK
dc.relation.ispartofPLoS Computational Biologyen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subject.meshCommunity-Acquired Infections - epidemiology - transmission - virology-
dc.subject.meshCross Infection - epidemiology - transmission - virology-
dc.subject.meshModels, Statistical-
dc.subject.meshSARS Virus-
dc.subject.meshSevere Acute Respiratory Syndrome - epidemiology - transmission-
dc.titleTemporal variability and social heterogeneity in disease transmission: The case of SARS in Hong Kongen_HK
dc.typeArticleen_HK
dc.identifier.emailLeung, GM:gmleung@hku.hken_HK
dc.identifier.authorityLeung, GM=rp00460en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1371/journal.pcbi.1000471en_HK
dc.identifier.pmid19696879-
dc.identifier.pmcidPMC2717369-
dc.identifier.scopuseid_2-s2.0-70049109406en_HK
dc.identifier.hkuros164204en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-70049109406&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume5en_HK
dc.identifier.issue8en_HK
dc.identifier.spagee1000471-
dc.identifier.epagee1000471-
dc.identifier.isiWOS:000270799700015-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridCori, A=34874728300en_HK
dc.identifier.scopusauthoridBoëlle, PY=7003593801en_HK
dc.identifier.scopusauthoridThomas, G=7404576265en_HK
dc.identifier.scopusauthoridLeung, GM=7007159841en_HK
dc.identifier.scopusauthoridValleron, AJ=7004672683en_HK

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