Please use this identifier to cite or link to this item: https://doi.org/10.1111/j.1467-9876.2005.05383.x
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dc.titleSemiparametric estimation of the duration of immunity from infectious disease time series: Influenza as a case-study
dc.contributor.authorXia, Y.
dc.contributor.authorGog, J.R.
dc.contributor.authorGrenfell, B.T.
dc.date.accessioned2014-10-28T05:14:57Z
dc.date.available2014-10-28T05:14:57Z
dc.date.issued2005
dc.identifier.citationXia, Y., Gog, J.R., Grenfell, B.T. (2005). Semiparametric estimation of the duration of immunity from infectious disease time series: Influenza as a case-study. Journal of the Royal Statistical Society. Series C: Applied Statistics 54 (3) : 659-672. ScholarBank@NUS Repository. https://doi.org/10.1111/j.1467-9876.2005.05383.x
dc.identifier.issn00359254
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105353
dc.description.abstractAn important epidemiological problem is to estimate the decay through time of immunity following infection. For this purpose, we propose a semiparametric time series epidemic model that is based on the mechanism of the susceptible-infected-recovered-susceptible system to analyse complex time series data. We develop an estimation method for the model. Simulations show that the approach proposed can capture the non-linearity of epidemics as well as estimate the decay of immunity. We apply our approach to influenza in France and the Netherlands and show a rapid decline in immunity following infection, which agrees with recent spatiotemporal analyses. © 2005 Royal Statistical Society.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1111/j.1467-9876.2005.05383.x
dc.sourceScopus
dc.subjectDynamical models in epidemics
dc.subjectGeneralized partially linear single-index model
dc.subjectImmunity
dc.subjectInfluenza
dc.subjectKernel smoother
dc.subjectSusceptible-infected-recovered-susceptible model
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1111/j.1467-9876.2005.05383.x
dc.description.sourcetitleJournal of the Royal Statistical Society. Series C: Applied Statistics
dc.description.volume54
dc.description.issue3
dc.description.page659-672
dc.identifier.isiut000228336500011
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