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

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

Abstract

Summary. 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.

Suggested Citation

  • Yingcun Xia & Julia R. Gog & Bryan T. Grenfell, 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, Royal Statistical Society, vol. 54(3), pages 659-672, June.
    • Handle: RePEc:bla:jorssc:v:54:y:2005:i:3:p:659-672
    DOI: 10.1111/j.1467-9876.2005.05383.x
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    Cited by:

    1. Razvan G. Romanescu & Rob Deardon, 2017. "Fast Inference for Network Models of Infectious Disease Spread," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(3), pages 666-683, September.

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