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Article: A Robust Parameter Estimation Method for Estimating Disease Burden of Respiratory Viruses

TitleA Robust Parameter Estimation Method for Estimating Disease Burden of Respiratory Viruses
Authors
Issue Date2014
PublisherPublic Library of Science. The Journal's web site is located at http://www.plosone.org/home.action
Citation
PLOS One, 2014, v. 9 n. 3, article no. e90126 How to Cite?
AbstractBACKGROUND: Poisson model has been widely applied to estimate the disease burden of influenza, but there has been little success in providing reliable estimates for other respiratory viruses. METHODS: We compared the estimates of excess hospitalization rates derived from the Poisson models with different combinations of inference methods and virus proxies respectively, with the aim to determine the optimal modeling approach. These models were validated by comparing the estimates of excess hospitalization attributable to respiratory viruses with the observed rates of laboratory confirmed paediatric hospitalization for acute respiratory infections obtained from a population based study. RESULTS: The Bayesian inference method generally outperformed the classical likelihood estimation, particularly for RSV and parainfluenza, in terms of providing estimates closer to the observed hospitalization rates. Compared to the other proxy variables, age-specific positive counts provided better estimates for influenza, RSV and parainfluenza, regardless of inference methods. The Bayesian inference combined with age-specific positive counts also provided valid and reliable estimates for excess hospitalization associated with multiple respiratory viruses in both the 2009 H1N1 pandemic and interpandemic period. CONCLUSIONS: Poisson models using the Bayesian inference method and virus proxies of age-specific positive counts should be considered in disease burden studies on multiple respiratory viruses.
Persistent Identifierhttp://hdl.handle.net/10722/197625
ISSN
2015 Impact Factor: 3.057
2015 SCImago Journal Rankings: 1.395
PubMed Central ID

 

DC FieldValueLanguage
dc.contributor.authorChan, KPen_US
dc.contributor.authorWong, CMen_US
dc.contributor.authorChiu, SSSen_US
dc.contributor.authorChan, KHen_US
dc.contributor.authorWang, XLen_US
dc.contributor.authorChan, ELYen_US
dc.contributor.authorPeiris, JSMen_US
dc.contributor.authorYang, Len_US
dc.date.accessioned2014-05-29T08:31:47Z-
dc.date.available2014-05-29T08:31:47Z-
dc.date.issued2014en_US
dc.identifier.citationPLOS One, 2014, v. 9 n. 3, article no. e90126en_US
dc.identifier.issn1932-6203-
dc.identifier.urihttp://hdl.handle.net/10722/197625-
dc.description.abstractBACKGROUND: Poisson model has been widely applied to estimate the disease burden of influenza, but there has been little success in providing reliable estimates for other respiratory viruses. METHODS: We compared the estimates of excess hospitalization rates derived from the Poisson models with different combinations of inference methods and virus proxies respectively, with the aim to determine the optimal modeling approach. These models were validated by comparing the estimates of excess hospitalization attributable to respiratory viruses with the observed rates of laboratory confirmed paediatric hospitalization for acute respiratory infections obtained from a population based study. RESULTS: The Bayesian inference method generally outperformed the classical likelihood estimation, particularly for RSV and parainfluenza, in terms of providing estimates closer to the observed hospitalization rates. Compared to the other proxy variables, age-specific positive counts provided better estimates for influenza, RSV and parainfluenza, regardless of inference methods. The Bayesian inference combined with age-specific positive counts also provided valid and reliable estimates for excess hospitalization associated with multiple respiratory viruses in both the 2009 H1N1 pandemic and interpandemic period. CONCLUSIONS: Poisson models using the Bayesian inference method and virus proxies of age-specific positive counts should be considered in disease burden studies on multiple respiratory viruses.-
dc.languageengen_US
dc.publisherPublic Library of Science. The Journal's web site is located at http://www.plosone.org/home.action-
dc.relation.ispartofPLOS Oneen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleA Robust Parameter Estimation Method for Estimating Disease Burden of Respiratory Virusesen_US
dc.typeArticleen_US
dc.identifier.emailChan, KP: kpchanaa@hku.hken_US
dc.identifier.emailWong, CM: hrmrwcm@hku.hken_US
dc.identifier.emailChiu, SSS: ssschiu@hku.hken_US
dc.identifier.emailChan, KH: chankh2@hkucc.hku.hken_US
dc.identifier.emailWang, XL: xinw@hku.hken_US
dc.identifier.emailChan, ELY: laiyin@hkucc.hku.hken_US
dc.identifier.emailPeiris, JSM: malik@hkucc.hku.hken_US
dc.identifier.authorityWong, CM=rp00338en_US
dc.identifier.authorityChiu, SSS=rp00421en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1371/journal.pone.0090126-
dc.identifier.pmid24651832-
dc.identifier.pmcidPMC3961249-
dc.identifier.hkuros229025en_US
dc.identifier.volume9en_US
dc.identifier.issue3en_US
dc.publisher.placeUnited States-

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