Mathematical models suggest that social distancing measures, such as school closures, may mitigate community transmission during an influenza pandemic. Because closures are disruptive to schools and families, they are rarely employed during seasonal influenza outbreaks. A rare circumstance enabled us to examine the association between school closure and absenteeism during a seasonal influenza outbreak when half of King County, Washington public schools closed for a winter recess 19-23 February 2007, while half remained open for all or part of the week.
Using absenteeism as a proxy for influenza activity, we tested the hypothesis that schools on break would experience lower rates of post-break absenteeism than schools remaining open. We conducted daily retrospective and prospective surveillance from 5 February-9 March 2007 in schools on break (n = 256) and in session (n = 205). We use generalized estimating equations with Poisson distribution to evaluate whether mean absenteeism after the break differed between schools on break and those in session, adjusting for baseline absenteeism and repeated measurements by schools over time.
Results indicate no difference in post-break absenteeism in schools on break compared with schools that remained in session (relative risk = 1.07 [95% confidence interval = 0.96-1.20]). This result held in elementary schools (1.00 [0.91-1.10]), where absenteeism patterns are thought to be most representative of community influenza activity.
We did not find that school closure during a seasonal influenza outbreak reduced subsequent absenteeism. However, limitations in this "natural experiment" hampered our ability to detect a benefit if one truly was present.
[Show abstract][Hide abstract] ABSTRACT: Background
School environments are thought to play an important role in the community spread of infectious diseases such as influenza because of the high mixing rates of school children. The closure of schools has therefore been proposed as an efficient mitigation strategy. Such measures come however with high associated social and economic costs, making alternative, less disruptive interventions highly desirable. The recent availability of high-resolution contact network data from school environments provides an opportunity to design models of micro-interventions and compare the outcomes of alternative mitigation measures.
Methods and results
We model mitigation measures that involve the targeted closure of school classes or grades based on readily available information such as the number of symptomatic infectious children in a class. We focus on the specific case of a primary school for which we have high-resolution data on the close-range interactions of children and teachers. We simulate the spread of an influenza-like illness in this population by using an SEIR model with asymptomatics, and compare the outcomes of different mitigation strategies. We find that targeted class closure affords strong mitigation effects: closing a class for a fixed period of time – equal to the sum of the average infectious and latent durations – whenever two infectious individuals are detected in that class decreases the attack rate by almost 70% and significantly decreases the probability of a severe outbreak. The closure of all classes of the same grade mitigates the spread almost as much as closing the whole school.
Our model of targeted class closure strategies based on readily available information on symptomatic subjects and on limited information on mixing patterns, such as the grade structure of the school, shows that these strategies might be almost as effective as whole-school closure, at a much lower cost. This may inform public health policies for the management and mitigation of influenza-like outbreaks in the community.
Electronic supplementary material
The online version of this article (doi:10.1186/s12879-014-0695-9) contains supplementary material, which is available to authorized users.
"A study conducted by Cauchemez et al. showed a relation between school vacation and a decrease in infection rates in France ; Heymann et al. were able to show similar results for the 1999/2000 influenza epidemic in Israel when teachers went on strike during the influenza season . However, another study conducted by Rodriguez et al. failed to show such an association in a survey about absenteeism in King County (Washington) public schools . Hence, there might be a real drop in cases during the Swiss Christmas vacations due to school closure, but such an effect is neither proven nor quantified. "
[Show abstract][Hide abstract] ABSTRACT: ABSTRACT: world has not faced a severe pandemic for decades, except the rather mild H1N1 one in 2009, pandemic influenza models are inherently hypothetical and validation is, thus, difficult. We aim at reconstructing a recent seasonal influenza epidemic that occurred in Switzerland and deem this to be a promising validation strategy for models of influenza spread.
We present a spatially explicit, individual-based simulation model of influenza spread. The simulation model bases upon (i) simulated human travel data, (ii) data on human contact patterns and (iii) empirical knowledge on the epidemiology of influenza. For model validation we compare the simulation outcomes with empirical knowledge regarding (i) the shape of the epidemic curve, overall infection rate and reproduction number, (ii) age-dependent infection rates and time of infection, (iii) spatial patterns.
The simulation model is capable of reproducing the shape of the 2003/2004 H3N2 epidemic curve of Switzerland and generates an overall infection rate (14.9 percent) and reproduction numbers (between 1.2 and 1.3), which are realistic for seasonal influenza epidemics. Age and spatial patterns observed in empirical data are also reflected by the model: Highest infection rates are in children between 5 and 14 and the disease spreads along the main transport axes from west to east.
We show that finding evidence for the validity of simulation models of influenza spread by challenging them with seasonal influenza outbreak data is possible and promising. Simulation models for pandemic spread gain more credibility if they are able to reproduce seasonal influenza outbreaks. For more robust modelling of seasonal influenza, serological data complementing sentinel information would be beneficial.
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