title: Model-Based Comprehensive Analysis of School Closure Policies for Mitigating Influenza Epidemics and Pandemics.
abstract: School closure policies are among the non-pharmaceutical measures taken into consideration to mitigate influenza epidemics and pandemics spread. However, a systematic review of the effectiveness of alternative closure policies has yet to emerge. Here we perform a model-based analysis of four types of school closure, ranging from the nationwide closure of all schools at the same time to reactive gradual closure, starting from class-by-class, then grades and finally the whole school. We consider policies based on triggers that are feasible to monitor, such as school absenteeism and national ILI surveillance system. We found that, under specific constraints on the average number of weeks lost per student, reactive school-by-school, gradual, and county-wide closure give comparable outcomes in terms of optimal infection attack rate reduction, peak incidence reduction or peak delay. Optimal implementations generally require short closures of one week each; this duration is long enough to break the transmission chain without leading to unnecessarily long periods of class interruption. Moreover, we found that gradual and county closures may be slightly more easily applicable in practice as they are less sensitive to the value of the excess absenteeism threshold triggering the start of the intervention. These findings suggest that policy makers could consider school closure policies more diffusely as response strategy to influenza epidemics and pandemics, and the fact that some countries already have some experience of gradual or regional closures for seasonal influenza outbreaks demonstrates that logistic and feasibility challenges of school closure strategies can be to some extent overcome.
authors.affiliation: Bruno Kessler Foundation, Trento, Italy.
authors.forename: Laura
authors.initials: L
authors.lastname: Fumanelli
authors.affiliation: Bruno Kessler Foundation, Trento, Italy.
authors.forename: Marco
authors.initials: M
authors.lastname: Ajelli
authors.affiliation: Bruno Kessler Foundation, Trento, Italy.
authors.forename: Stefano
authors.initials: S
authors.lastname: Merler
authors.affiliation: MRC Centre for Outbreak Analysis and Modelling, School of Public Health, Imperial College London, London, United Kingdom.
authors.forename: Neil M
authors.initials: NM
authors.lastname: Ferguson
authors.affiliation: Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France.
authors.forename: Simon
authors.initials: S
authors.lastname: Cauchemez
mesh_terms: D000041:Absenteeism; D019295:Computational Biology; D003198:Computer Simulation; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology / Q000517:prevention & control* / Q000635:transmission; D008954:Models, Biological; D058873:Pandemics / Q000517:prevention & control* / Q000706:statistics & numerical data; D012574:Schools; D013334:Students / Q000706:statistics & numerical data