Estimating the impact of school closure on influenza transmission from Sentinel data

By Simon Cauchemez, A. Valleron, Pierre-Yves Boëlle, A. Flahault, Neil M. Ferguson. Nature, 2008

699 citations recorded by the source corpus.

Subfields: Agronomy and Crop Science, Epidemiology, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/4799c6f576271262dc15be23eb20f24f4926ceb8

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Citations in the corpus

Projecting social contact matrices in 152 countries using contact surveys and demographic data (2017, methodology): “Simulation studies using the POLYMOD data [23] and others [26,45,46] support this conclusion and explain why children and teenagers were the major channels for the initial transmission of infection during the 2009 influenza A(H1N1) pandemic [47], and why school closure is one of the main non-pharmac”
Networks and the Epidemiology of Infectious Disease (2010, background): “This assumed that the source of infection was correctly identified by the fi eld investigators, thereby giving an empirical estimate of the probability of infectio n as a function of distance (Ferguson et al., 2001; Keeling et al., 2001; Savill et al., 2 006). Strikingly, this shows a high degree of”
Biips: Software for Bayesian Inference with Interacting Particle Systems (2014, background): “…economics and macro-economics (Pitt and Shephard 1999; Fernández-Villaverde and Rubio-Ramírez 2007; Flury and Shephard 2011; Del Moral et al. 2012), epidemiology (Cauchemez et al. 2008; Dureau et al. 2013), ecology (Buckland et al. 2007; Peters et al. 2010) or pharmacology (Donnet and Samson 2011).”
Decreased influenza activity during the COVID-19 pandemic—United States, Australia, Chile, and South Africa, 2020 (2020, background): “Although school-aged children can drive the spread of influenza, the effectiveness of school closures alone is not clear because adults have other exposures (8).”
Plug-and-play inference for disease dynamics: measles in large and small populations as a case study (2009, background): “…recent development of likelihood-based approaches capable of dealing with the full spectrum of unavoidable complexities (Cauchemez & Ferguson 2008; Cauchemez et al. 2008; King et al. 2008)—unobserved variables, measurement error, process noise, nonlinearity, non-stationarity and covariates—means…”
Absolute Humidity and the Seasonal Onset of Influenza in the Continental United States (2010, background): “, more time indoors in close contact during winter [19]), and host resistance (e.”