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We consider a study duration of three to four years, during which a single antigenic type of influenza would be circulating, and we evaluate our ability to reconstruct disease dynamics based on serological data alone.
Recently, our group developed a simple method to reliably differentiate primary and secondary acute dengue infections based on serological data (IgG ELISA) [19] and quickly identify individuals with a secondary dengue infection, which is considered as a risk factor.
Estimation on the infection rates based on serological data may be affected by confounding factor of cross-reactive antibody-rising from seasonal influenza vaccination; and probably from pre-existing antibody against the 1957 influenza A (H1N1) virus.
Finally, the resulting contact patterns could prove useful to improve the estimation of transmission parameters for airborne infections based on serological data as already shown by [ 5].
A way of addressing this would be to calculate the attack rates based on serological data now being collected by HPA CfI.
We show that certain intuitive assumptions about disease dynamics need to be revisited when dynamical inference is based on serological data, the reason being that observations in an SSE study are made on recovered/susceptible individuals instead of infected individuals.
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While this assumption was based on serological response data obtained from a long-term immunogenicity study [ 11], this assumption was varied in a sensitivity analysis.
Based solely on serological data, previous studies in our group and others have shown that lower baseline levels of influenza-specific antibody and vaccination with TIV rather than LAIV were associated with greater antibody response after vaccination [8], [41], [42].
Second, that preopera-tive risk assessment based on serological and clinical chemistry data is possible, with high levels of accuracy.
These data are slightly different than those that appeared previously as Figure 1 in [15] and include some reclassified cases based on serological tests (S. Ma, unpublished data).
The first system is based on serological recognition.
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based on unimputed data
based on secondary data
based on fragmentary data
based on serological titres
based on solid data
based on serological results
based on serological reactions
based on serological methods
based on clear data
based on serological assignations
based on raw data
based on empirical data
based on serological findings
based on fictitious data
based on good data
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