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Journal ArticleDOI

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

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TLDR
It is shown that holidays lead to a 20–29% reduction in the rate at which influenza is transmitted to children, but that they have no detectable effect on the contact patterns of adults, as well as predicting the effect of school closure during a pandemic.
Abstract
The threat posed by the highly pathogenic H5N1 influenza virus requires public health authorities to prepare for a human pandemic. Although pre-pandemic vaccines and antiviral drugs might significantly reduce illness rates, their stockpiling is too expensive to be practical for many countries. Consequently, alternative control strategies, based on non-pharmaceutical interventions, are a potentially attractive policy option. School closure is the measure most often considered. The high social and economic costs of closing schools for months make it an expensive and therefore controversial policy, and the current absence of quantitative data on the role of schools during influenza epidemics means there is little consensus on the probable effectiveness of school closure in reducing the impact of a pandemic. Here, from the joint analysis of surveillance data and holiday timing in France, we quantify the role of schools in influenza epidemics and predict the effect of school closure during a pandemic. We show that holidays lead to a 20-29% reduction in the rate at which influenza is transmitted to children, but that they have no detectable effect on the contact patterns of adults. Holidays prevent 16-18% of seasonal influenza cases (18-21% in children). By extrapolation, we find that prolonged school closure during a pandemic might reduce the cumulative number of cases by 13-17% (18-23% in children) and peak attack rates by up to 39-45% (47-52% in children). The impact of school closure would be reduced if it proved difficult to maintain low contact rates among children for a prolonged period.

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Journal ArticleDOI

Influenza-like illness in a community surrounding a school-based outbreak of 2009 pandemic influenza A (H1N1) virus-Chicago, Illinois, 2009

TL;DR: A household survey was performed to estimate the ILI attack rate among community residents and compared reported ILI with confirmed pH1N1 cases and ILI surveillance data (ie, hospital ILI visits, influenza testing, and school absenteeism) to help assess the burden of ILI in a community.
Journal ArticleDOI

Breaking the waves: modelling the potential impact of public health measures to defer the epidemic peak of novel influenza A/H1N1.

TL;DR: A deterministic SEIR model is constructed using the age distribution and size of the population of Germany based on the observed number of imported cases and the early findings for the epidemiologic characteristics described by Fraser to propose a two-step control strategy.
Journal ArticleDOI

Comparing Observed with Predicted Weekly Influenza-Like Illness Rates during the Winter Holiday Break, United States, 2004-2013

TL;DR: While most regions had ILI rates higher than predicted during winter holiday break and lower than predicted after the break during the 2012–2013 season, overall there was not a consistent relationship between observed and predicted ILI around the winter holidaybreak during the other influenza seasons.
Journal ArticleDOI

Modeling the effect of school closures in a pandemic scenario: exploring two different contact matrices.

TL;DR: Unless vaccine is available early, school closure alone may not be able to delay the peak until vaccine is ready to be deployed, and if vaccination begins quickly, schoolclosure may be helpful in providing the time to vaccinate school-aged children before the pandemic peaks.
References
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BookDOI

Markov Chain Monte Carlo in Practice

TL;DR: The Markov Chain Monte Carlo Implementation Results Summary and Discussion MEDICAL MONITORING Introduction Modelling Medical Monitoring Computing Posterior Distributions Forecasting Model Criticism Illustrative Application Discussion MCMC for NONLINEAR HIERARCHICAL MODELS.
BookDOI

Sequential Monte Carlo methods in practice

TL;DR: This book presents the first comprehensive treatment of Monte Carlo techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection.
Book

Monte Carlo strategies in scientific computing

Jun Liu
TL;DR: This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared.
Journal ArticleDOI

Strategies for mitigating an influenza pandemic

TL;DR: It is found that border restrictions and/or internal travel restrictions are unlikely to delay spread by more than 2–3 weeks unless more than 99% effective, and vaccine stockpiled in advance of a pandemic could significantly reduce attack rates even if of low efficacy.
Journal ArticleDOI

Monte Carlo Strategies in Scientific Computing

Tim Hesterberg
- 01 Nov 2002 - 
TL;DR: The strength of this book is in bringing together advanced Monte Carlo methods developed in many disciplines, including the Ising model, molecular structure simulation, bioinformatics, target tracking, hypothesis testing for astronomical observations, Bayesian inference of multilevel models, missing-data problems.
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