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Estimating influenza attack rates in the United States using a participatory cohort

We considered how participatory syndromic surveillance data can be used to estimate influenza attack rates during the 2012–2013 and 2013–2014 seasons in the United States. Our inference is based on assessing the difference in the rates of self-reported influenza-like illness (ILI, defined as presenc...

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Detalles Bibliográficos
Autores principales: Chunara, Rumi, Goldstein, Edward, Patterson-Lomba, Oscar, Brownstein, John S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4894435/
https://www.ncbi.nlm.nih.gov/pubmed/25835538
http://dx.doi.org/10.1038/srep09540
Descripción
Sumario:We considered how participatory syndromic surveillance data can be used to estimate influenza attack rates during the 2012–2013 and 2013–2014 seasons in the United States. Our inference is based on assessing the difference in the rates of self-reported influenza-like illness (ILI, defined as presence of fever and cough/sore throat) among the survey participants during periods of active vs. low influenza circulation as well as estimating the probability of self-reported ILI for influenza cases. Here, we combined Flu Near You data with additional sources (Hong Kong household studies of symptoms of influenza cases and the U.S. Centers for Disease Control and Prevention estimates of vaccine coverage and effectiveness) to estimate influenza attack rates. The estimated influenza attack rate for the early vaccinated Flu Near You members (vaccination reported by week 45) aged 20–64 between calendar weeks 47–12 was 14.7%(95% CI(5.9%,24.1%)) for the 2012–2013 season and 3.6%(−3.3%,10.3%) for the 2013–2014 season. The corresponding rates for the US population aged 20–64 were 30.5% (4.4%, 49.3%) in 2012–2013 and 7.1%(−5.1%, 32.5%) in 2013–2014. The attack rates in women and men were similar each season. Our findings demonstrate that participatory syndromic surveillance data can be used to gauge influenza attack rates during future influenza seasons.