The impact of regular school closure on seasonal influenza epidemics: a data-driven spatial transmission model for Belgium

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Abstract

Background

School closure is often considered as an option to mitigate influenza epidemics because of its potential to reduce transmission in children and then in the community. The policy is still however highly debated because of controversial evidence. Moreover, the specific mechanisms leading to mitigation are not clearly identified.

Methods

We introduced a stochastic spatial age-specific metapopulation model to assess the role of holiday-associated behavioral changes and how they affect seasonal influenza dynamics. The model is applied to Belgium, parameterized with country-specific data on social mixing and travel, and calibrated to the 2008/2009 influenza season. It includes behavioral changes occurring during weekend vs. weekday, and holiday vs. school-term. Several experimental scenarios are explored to identify the relevant social and behavioral mechanisms.

Results

Stochastic numerical simulations show that holidays considerably delay the peak of the season and mitigate its impact. Changes in mixing patterns are responsible for the observed effects, whereas changes in travel behavior do not alter the epidemic. Weekends are important in slowing down the season by periodically dampening transmission. Christmas holidays have the largest impact on the epidemic, however later school breaks may help in reducing the epidemic size, stressing the importance of considering the full calendar. An extension of the Christmas holiday of 1 week may further mitigate the epidemic.

Conclusion

Changes in the way individuals establish contacts during holidays are the key ingredient explaining the mitigating effect of regular school closure. Our findings highlight the need to quantify these changes in different demographic and epidemic contexts in order to provide accurate and reliable evaluations of closure effectiveness. They also suggest strategic policies in the distribution of holiday periods to minimize the epidemic impact.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-017-2934-3) contains supplementary material, which is available to authorized users.

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Most cited references 80

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Modelling the influence of human behaviour on the spread of infectious diseases: a review.

Sebastian Funk, Marcel Salathé, Vincent A. A. Jansen (2010)

Human behaviour plays an important role in the spread of infectious diseases, and understanding the influence of behaviour on the spread of diseases can be key to improving control efforts. While behavioural responses to the spread of a disease have often been reported anecdotally, there has been relatively little systematic investigation into how behavioural changes can affect disease dynamics. Mathematical models for the spread of infectious diseases are an important tool for investigating and quantifying such effects, not least because the spread of a disease among humans is not amenable to direct experimental study. Here, we review recent efforts to incorporate human behaviour into disease models, and propose that such models can be broadly classified according to the type and source of information which individuals are assumed to base their behaviour on, and according to the assumed effects of such behaviour. We highlight recent advances as well as gaps in our understanding of the interplay between infectious disease dynamics and human behaviour, and suggest what kind of data taking efforts would be helpful in filling these gaps.

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

Simon Cauchemez, Alain-Jacques Valleron, Pierre-Yves Boëlle … (2008)

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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Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents.

Jacco Wallinga, Peter Teunis, Mirjam Kretzschmar (2006)

The estimation of transmission parameters has been problematic for diseases that rely predominantly on transmission of pathogens from person to person through small infectious droplets. Age-specific transmission parameters determine how such respiratory agents will spread among different age groups in a human population. Estimating the values of these parameters is essential in planning an effective response to potentially devastating pandemics of smallpox or influenza and in designing control strategies for diseases such as measles or mumps. In this study, the authors estimated age-specific transmission parameters by augmenting infectious disease data with auxiliary data on self-reported numbers of conversational partners per person. They show that models that use transmission parameters based on these self-reported social contacts are better able to capture the observed patterns of infection of endemically circulating mumps, as well as observed patterns of spread of pandemic influenza. The estimated age-specific transmission parameters suggested that school-aged children and young adults will experience the highest incidence of infection and will contribute most to further spread of infections during the initial phase of an emerging respiratory-spread epidemic in a completely susceptible population. These findings have important implications for controlling future outbreaks of novel respiratory-spread infectious agents.

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Author and article information

Contributors

Giancarlo De Luca: giancarlo.de-luca@inserm.fr

Kim Van Kerckhove: kim.vankerckhove@uhasselt.be

Pietro Coletti: pietro.coletti@uhasselt.be

Chiara Poletto: chiara.poletto@inserm.fr

Nathalie Bossuyt: nathalie.bossuyt@wiv-isp.be

Niel Hens: niel.hens@uhasselt.be

Vittoria Colizza:

ORCID: http://orcid.org/0000-0002-2113-2374

vittoria.colizza@inserm.fr

Journal

Journal ID (nlm-ta): BMC Infect Dis

Journal ID (iso-abbrev): BMC Infect. Dis

Title: BMC Infectious Diseases

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2334

Publication date (Electronic): 10 January 2018

Publication date PMC-release: 10 January 2018

Publication date Collection: 2018

Volume: 18

Electronic Location Identifier: 29

Affiliations

[1 ]Sorbonne Universités, UPMC Univ. Paris 06, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique (IPLESP UMR-S 1136), Paris, 75012 France

[2 ]ISNI 0000 0001 0604 5662, GRID grid.12155.32, Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Agoralaan Gebouw D, ; Diepenbeek, 3590 Belgium

[3 ]ISNI 0000 0004 0635 3376, GRID grid.418170.b, Scientific Institute of Public Health (WIV-ISP), Public Health and Surveillance Directorate, Epidemiology of infectious diseases Service, ; Rue Juliette/Wytsmanstraat 14, Brussels, 1050 Belgium

[4 ]ISNI 0000 0001 0790 3681, GRID grid.5284.b, Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, ; Universiteitsplein 1, Wilrijk, 2610 Belgium

[5 ]ISNI 0000 0004 1759 3658, GRID grid.418750.f, ISI Foundation, ; Torino, 10126 Italy

Author information

Vittoria Colizza http://orcid.org/0000-0002-2113-2374

Article

Publisher ID: 2934

DOI: 10.1186/s12879-017-2934-3

PMC ID: 5764028

PubMed ID: 29321005

SO-VID: ad77f66d-6cda-4f7e-b428-a12527d5bdaa

License:

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 19 April 2017

Date accepted : 20 December 2017

Funding

Funded by: Agence Nationale de la Recherche (FR)

Award ID: ANR-12-MONU-0018

Funded by: Agence Nationale de la Recherche (FR)

Award ID: ANR-12-MONU-0018

Funded by: FundRef http://dx.doi.org/10.13039/100011272, FP7 Health;

Award ID: 278433

Funded by: FundRef http://dx.doi.org/10.13039/100010663, H2020 European Research Council;

Award ID: 682540

Funded by: FundRef http://dx.doi.org/10.13039/100010663, H2020 European Research Council;

Award ID: 682540

Custom metadata

ScienceOpen disciplines: Infectious disease & Microbiology

Keywords: influenza,metapopulation,epidemic modeling,spatial transmission,school closure

Data availability:

ScienceOpen disciplines: Infectious disease & Microbiology

Keywords: influenza, metapopulation, epidemic modeling, spatial transmission, school closure

The impact of regular school closure on seasonal influenza epidemics: a data-driven spatial transmission model for Belgium

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Mary Ann Liebert: COVID-19 science

Most cited references 80

Modelling the influence of human behaviour on the spread of infectious diseases: a review.

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

Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents.

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