Statistical identifiability and sample size calculations for serial seroepidemiology

Epidemics
Dao Nguyen Vinh, Maciej F Boni

Abstract

Inference on disease dynamics is typically performed using case reporting time series of symptomatic disease. The inferred dynamics will vary depending on the reporting patterns and surveillance system for the disease in question, and the inference will miss mild or underreported epidemics. To eliminate the variation introduced by differing reporting patterns and to capture asymptomatic or subclinical infection, inferential methods can be applied to serological data sets instead of case reporting data. To reconstruct complete disease dynamics, one would need to collect a serological time series. In the statistical analysis presented here, we consider a particular kind of serological time series with repeated, periodic collections of population-representative serum. We refer to this study design as a serial seroepidemiology (SSE) design, and we base the analysis on our epidemiological knowledge of influenza. 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. We show that the processes of reinfection, antibody generation, and antibody waning confound each other and ...Continue Reading

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Citations

Aug 21, 2018·PLoS Biology·Adam J KucharskiSteven Riley
Jul 22, 2017·Scientific Reports·Nguyen Thi Duy NhatMaciej F Boni

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Software Mentioned

Matlab

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