School’s Out: Seasonal Variation in the Movement Patterns of School Children
School's Out: Seasonal Variation in the Movement Patterns of School Children
Adam J. Kucharski 0 2 3
Andrew J. K. Conlan 0 2 3
Ken T. D. Eames 0 2 3
0 1 Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine , London , UK , 2 Fogarty International Center, National Institutes of Health , Bethesda , USA , 3 Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge , Cambridge , UK
1 rapidd). AJKC was supported by the Alborada Trust (
2 Funding: AJK was supported by the Medical Research Council (fellowship MR/K021524/1
3 Academic Editor: Alain Barrat, Centre de Physique Théorique , FRANCE
School children are core groups in the transmission of many common infectious diseases, and are likely to play a key role in the spatial dispersal of disease across multiple scales. However, there is currently little detailed information about the spatial movements of this epidemiologically important age group. To address this knowledge gap, we collaborated with eight secondary schools to conduct a survey of movement patterns of school pupils in primary and secondary schools in the United Kingdom. We found evidence of a significant change in behaviour between term time and holidays, with term time weekdays characterised by predominately local movements, and holidays seeing much broader variation in travel patterns. Studies that use mathematical models to examine epidemic transmission and control often use adult commuting data as a proxy for population movements. We show that while these data share some features with the movement patterns reported by school children, there are some crucial differences between the movements of children and adult commuters during both term-time and holidays.
The social mixing behaviour of school-aged children is believed to be an important driving
factor of infectious disease spread. Levels of susceptibility and social mixing are likely to be higher
amongst school children than any other age group, and the relatively high levels of incidence of
many common infections in children identifies them as an important epidemiological group
Significant efforts have been made in recent years to quantify human social mixing patterns
with a view to improving our understanding of the transmission dynamics of infection [6–11].
There has been a particular focus on quantifying the amount of interaction between different
age groups. Studies have consistently shown that school-aged children make large numbers of
social contacts, predominantly with other school-aged children [8, 12]. Recent work has
quantified the extent to which mixing patterns change during school holiday periods; as might be
expected, children make fewer contacts during school holidays, with the greatest reduction
being in the number of contacts with other children [13, 14].
design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing Interests: The authors have declared
that no competing interests exist.
These social mixing studies have provided the essential information necessary to interpret
age-stratified incidence patterns of common infectious diseases. Likewise, information on the
movement of individuals is essential for understanding, and predicting, the spatial spread of
infection. Mathematical models have been developed to predict the spatial dispersal of infection
based upon human movement data [15–19]. Currently, available movement data are largely
restricted to commuting information [20–22], long distance travel (e.g. air-travel [23, 24]) and a
variety of proxy measures, from banknotes  to mobile phones . While all of these
sources are likely to be useful, none provide much information about the movements of
school-aged children, the key drivers of transmission for many common infectious diseases.
Some countries, such as Switzerland, do conduct more detailed diary-based studies of travel
patterns , which have been used to inform mathematical models [28, 29]. However, such
data is not yet available for the United Kingdom.
Here, we present the results of a study of the spatial movement patterns of school children,
carried out in sixteen schools across the UK. Movements were measured both during school
term times and school holidays, allowing a picture to be built up of the seasonal changes in
spatial mixing behaviour of children.
We worked with groups of schools during the 2011–12 and 2012–13 academic years to devise
and carry out a project to measure movement patterns of school children during term time and
school holidays. The methodology was based on an earlier project in which secondary school
pupils quantified the social networks present in their local primary schools .
During each academic year we worked alongside 4 secondary schools to design a
questionnaire, collect, and analyse data. Interaction was mainly through video-conferences, which
allowed the school students to share ideas with each other as well as with the researchers, and
visits from the researchers to the schools. The project was conceived partly as public
engagement, partly as outreach, and partly as research ; one of the main aims of the project was to
give support to students in developing their research questions and methods.
During the development phase of the study, students worked to devise a survey that would
quantify the distance travelled by school pupils, both in term time and holidays. The final
questionnaire was agreed upon by all schools, and a consistent version used throughout the project.
It was intended to use the questionnaire in both secondary and primary schools, so it was
designed to be as simple to complete as possible. Pupils and schools planned the details of their
engagement with participants to best suit their local circumstances.
The final survey asked participants to report, for each day over a two week period (including
one week of term time and one week of school holidays), the furthest they were from home
that day. The question was posed both as a set of distance options (at home all day; < 1 mile;
1–5 miles; 5–30 miles; 30–100 miles; > 100 miles) and by asking for the location (either as a
place name, street name, or postcode). Where places were reported, these were converted into
postcodes (using the first half of the postcode only) where possible.
Questionnaires were distributed with explanation at start of the fortnight, and could be filled
in by participants or by their parents on their behalf, or by a combination of the two; it was left
for individual families to decide on the approach that best suited them. To reduce recall errors,
participants were encouraged to complete the survey daily during the study period. In the first
year the study period included a week of the Easter holiday; in the second year the study period
included the week-long Spring half term holiday. This change was motivated both by the need
to fit the project in around the school timetable, and as a result of suggestions by participating
schools that it would lead to an improved return rate.
For analysis purposes, we categorised days as ‘term time weekdays’, ‘holiday weekdays’ or
‘weekends’ and compared reported movements during these periods by calculating the
proportion of reported trips that fell into each distance category. We calculated bootstrapped
confidence intervals for these proportions by repeatedly resampling the data with replacement to
obtain multiple alternate datasets of the same size. To compare school patterns with adult
movements, we also measured the commuting distances reported in the 2001 census in the 17
UK electoral wards that covered our study locations . Across these wards, in the 2001
census 12,792 adults reported commuting movements . Distances were measured from the
centroid of the home ward to the centroid of the work ward, and assigned to the same set of
categories as used in the school questionnaire, except that we did not include a category for ‘at
home’ because these data were not available.
In addition to providing estimates for the maximum distance travelled, participants were
also encouraged to report the (full or partial) postcode of the furthest location from home to
which they travelled. Postcodes were then geo-referenced to UK national grid co-ordinates
using the Office of National Statistics Postcode Directory (ONSPD). These data allowed the
creation of maps demonstrating the approximate location of movements. Locations with an
incomplete postcode were assigned a unique geo-location using the centroid of all partially
matching postcodes. Mapped locations were jittered uniformly within 2.5km squares to avoid
over plotting and to preserve anonymity. Maps were created using digital boundaries and
databases provided by the Office of National Statistics through the Open Geography portal.
Participation in this opt-in study was voluntary, and informed written consent was obtained
from parents/guardians on behalf of the children taking part in the study. All analysis was
carried out on anonymised data. The study was approved by the ethics committee of the London
School of Hygiene & Tropical Medicine.
During the 2011–12 and 2012–13 academic years, the travel movement questionnaire was
given to students in 16 schools in urban and rural locations around the UK. 825 questionnaires
were returned with sufficient information (age and/or year group of participant) to be included
in the final database (S1 dataset). 208 were returned in the first year, and 617 in the second
year. Details of the number of questionnaires given out by each school group were not available
in every case; the return rate was approximately 10% in the first year, and in the second year,
where we have this information, the return rate was approximately 42% (520 out of 1226
surveys returned). Returned surveys were reasonably complete; 2.6% of distance reports (2.9% in
year 1 and 2.5% in year 2) and 8.6% of postcode reports were missing (6.4% in year 1, 9.3% in
The geographic distribution of reported movements is shown in Fig 1. We found a distinct
difference in movement patterns between term time and holidays. During term-time weekdays,
most trips cluster around the home location, whereas travel patterns span a markedly wider
area during holidays and weekends. The long distance trips from Buckinghamshire to Cornwall
in term-time were the result of a class trip.
During both term time and holidays, the majority of daily movements were under 30 miles
(Figs 2A and 2B). However, we saw a significant difference between reported weekday
movements in term time and holidays (p < 0.0001 using chi-squared test with 6 degrees of freedom).
When compared to holidays, term time movements tended to cluster around short distances;
during term time, 76% of daily movements were in the under 1 mile and 1–5 miles categories,
compared to 40% in holidays. In holidays, daily movements display a broader distribution,
with an increase in both shorter and longer movements (27% of days were spent at home,
Fig 1. Furthest distance reported by participants during term weekdays (2,653 movements), holiday weekdays (3,086 movements) and weekends
(2,540 movements). Arrows indicate direction of travel; colours show location of different schools in the survey, grouped by district. Point locations are
jittered uniformly within 2.5km grids to preserve anonymity and reduce over plotting.
compared to 5% during term time, and 15% reported distances over 30 miles from home,
compared to 3% during term time). Weekend movements appear to be qualitatively more similar
to holiday movements than to term time movements (Fig 2C). When comparing our results
with commuting patterns reported in the 2001 Census in the electoral wards of our study
locations (Fig 2D), we found that adult commuters’ movements resemble school pupils’
movements during term time, with most trips between 1 and 5 miles. However, there were
significantly more long distance trips reported by adults in the Census than by children on
term weekdays in our study: 16% of reported commuter movements were over 30 miles.
We found that travel movements were different in Easter and half term holidays. During
the two-week Easter break, there were more trips of more than 100 miles, and fewer days spent
at home (S1 Fig). We also found some differences in travel movements between urban (i.e.
based within town or city) and rural schools. Although the overall pattern of movement during
term, holidays and weekends was similar, participants in urban areas made fewer long trips
during holiday weekdays, and spent fewer days at home at weekends than participants in rural
areas (S2 Fig).
The maximum distance travelled by an individual during the course of a holiday week was,
as might be expected, considerably further than the maximum distance travelled during a
term-time week (Figs 3A and 3B). During the holiday week, 31% of participants in the study
travelled at least 30 miles, compared with only 8% during the term week. There were also
significantly more participants who spent all week at home: 4% did so during the holidays, but
less than 1% during term. When comparing the maximum distance travelled during term time
and holidays at the individual level (Fig 3C), we see a clear tendency for participants to report
travelling further from home during holidays than during term time.
We also examined the accuracy of distance reporting. Participants reported a complete
postcode for 3,239 trips, of which 3,092 had matching self reported distances. This made it possible
to compare travel distance as measured by postcode with self-reporting for this subset of the
Fig 2. Comparison of reported movement distances. (A) Travel patterns for school children during holiday weekdays (Mon-Fri), with error bars showing
bootstrapped 95% confidence intervals; (B) term weekdays; (C) weekends; (D) adult commuting patterns in same locations as our study, as reported in 2001
data. Each origin and destination was assigned a spatial location as the centroid of the reported
postcode area. The Euclidean distance between these centroids was used to calculate a distance
category for each trip using the same categories as for self reporting. There was some possible
misclassification in all categories. However, overall 85% of trips (2633 reported movements)
differed by one or fewer categories from the postcode-estimated distance. The distributions of
discrepancies according to distance category are shown in S3 Fig.
Fig 3. Maximum travel distance reported by each individual over five weekdays (Mon-Fri). (A) Holiday
weekdays, error bars show bootstrapped 95% confidence intervals; (B) term weekdays; (C) histogram of
maximum distances travelled in term and holiday for each individual in the study. Colour indicates proportion
of individuals who travelled each pair of distances.
This study provides detailed information about the movement patterns of school children in
the United Kingdom. We have quantified the range of distances travelled during school term
time and school holidays, and shown that holidays are associated with both more days spent at
home and with more long distance movements than term time. These patterns are significantly
different to those found in the commuting data [20–22] commonly used to parameterise spatial
models of human disease transmission. Given the importance of children as drivers of infection
[1–5], our results suggest that the geographical spread of disease could in reality differ greatly
from existing model predictions.
There are some limitations to our study. Data were self-reported, and it is not clear how
accurately participants could identify the distances they travelled. However, assistance was given
using relevant local examples when surveys were distributed, and it is likely that participants
could make reasonable comparisons between different journeys, which would mean that the
trend is reported consistently. The response rate was disappointingly low in some schools,
which is perhaps inevitable given the nature of the survey, which had to be completed over the
course of a fortnight. We considered the use of web-based data collection, but took the view
that this would still require active participation over many days and would potentially exclude
participants on access grounds. A survey that could have been answered as a one-off would
likely have produced a better response , but we would not be confident that activities
would have been accurately recalled and, as we have found, there is value in collecting
information about movements each day as opposed to—for example—simply asking about the longest
movement over the course of a week. Moreover, while movement patterns of school-aged
participants appear to be different from reported commuting patterns (Fig 2), weekend and
holiday trips made by children are likely to be associated the travel patterns of the parents/
guardians. Hence our results suggest that the out-of-term travel patterns of certain adults also
differ from the movements reported by commuters in the 2001 Census.
The study was carried out by different student researchers in different schools, and survey
methodology may not have been identical in each case. However, many observed differences
between schools are likely to be genuine. For example, we found evidence that the movement
patterns in large towns were typically more localised than those in rural settings, and that more
long distance trips took place in the Easter holiday than in the Spring half term holiday.
Therefore it may be inappropriate to model movement patterns by assuming that all holiday periods
are identical. However, some effects may have been the result of other unmeasured factors (e.g.
local leisure provision or social-demographic factors). Ideally, we would repeat the survey in
the same school throughout the year, but this was not feasible within the time constraints of
Despite these limitations, the implications of the results for infectious disease spread are
clear. Longer distance movements during holidays offer the potential for infection to move
from place to place; such translocations of infection could be facilitated by the movement of
either susceptible or infected individuals. Although infected individuals may modify their
behaviour , the movement patterns described here would apply to pathogens that lead to mild or
asymptomatic infections, or those for which individuals are infectious before they are
Our results suggest that term time is associated with intense local spread, whereas holidays,
although involving a lower absolute contact rate , present opportunities for infection to
move long distances. The change in movement patterns is likely to be particularly influential in
the case of a school-based outbreak that reaches a high level just before a holiday period, thus
releasing a large number of cases to seed infection to new parts of the country. In such an
example, school closure—while having the potential benefit of reducing the local force of
infection—might have a different effect when considered at a larger spatial scale.
S2 Fig. Comparison of reported movement distances in urban and rural settings. (A) Travel
patterns for school children during holiday weekdays (Mon-Fri) in urban areas, with error bars
showing bootstrapped 95% confidence intervals; (B) urban term weekdays; (C) urban
weekends; (D) rural term weekdays; (E) rural weekends; (F) rural holiday weekdays.
S3 Fig. Distribution of the difference between postcode distances and reported distances.
Postcode distance is defined as the centroid of the origin postcode area to the centroid of the
destination postcode area. Reported distance is the category given from six possible responses
in the survey. The difference is the category of the postcode distance minus the reported
distance, shown for each of the six distance categories.
We would like to thank Jenny Gage, Julia Hawkins, and the Millennium Mathematics Project,
and the staff and students of Greenford High School, Humphry Davy School, King James’s
School, Meole Brace School Science College, Rushey Mead School, Sir William Borlase’s
Grammar School, Stanborough School and Villiers High School. Census output is Crown copyright
and is reproduced with the permission of the Controller of HMSO and the Queen’s Printer for
Scotland. The postcode database contains National Statistics data and Ordnance Survey data
Crown copyright and database right 2013.
Conceived and designed the experiments: AJK AJKC KTDE. Performed the experiments: AJK
AJKC KTDE. Analyzed the data: AJK AJKC KTDE. Wrote the paper: AJK AJKC KTDE.
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