Semiparametric estimation of the duration of immunity from infectious disease time series: Influenza as a case-study

Yingcun Xia; Julia R. Gog; Bryan T. Grenfell

doi:10.1111/j.1467-9876.2005.05383.x

Semiparametric estimation of the duration of immunity from infectious disease time series: Influenza as a case-study

Yingcun Xia, Julia R. Gog, Bryan T. Grenfell

Research output: Contribution to journal › Article › peer-review

36 Scopus citations

Abstract

An 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.

Original language	English (US)
Pages (from-to)	659-672
Number of pages	14
Journal	Journal of the Royal Statistical Society. Series C: Applied Statistics
Volume	54
Issue number	3
DOIs	https://doi.org/10.1111/j.1467-9876.2005.05383.x
State	Published - 2005
Externally published	Yes

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty

Keywords

Dynamical models in epidemics
Generalized partially linear single-index model
Immunity
Influenza
Kernel smoother
Susceptible-infected-recovered-susceptible model

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10.1111/j.1467-9876.2005.05383.x

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title = "Semiparametric estimation of the duration of immunity from infectious disease time series: Influenza as a case-study",

abstract = "An 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.",

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year = "2005",

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AU - Xia, Yingcun

AU - Gog, Julia R.

AU - Grenfell, Bryan T.

PY - 2005

Y1 - 2005

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AB - An 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.

KW - Dynamical models in epidemics

KW - Generalized partially linear single-index model

KW - Immunity

KW - Influenza

KW - Kernel smoother

KW - Susceptible-infected-recovered-susceptible model

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Semiparametric estimation of the duration of immunity from infectious disease time series: Influenza as a case-study

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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