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 | |
| 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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