1887

Abstract

Aggregation of children in schools has been established to be a key driver of transmission of infectious diseases. Mathematical models of transmission used to predict the impact of control measures, such as vaccination and testing, commonly depend on self-reported contact data. However, the link between self-reported social contacts and pathogen transmission has not been well described. To address this, we used as a model organism to track transmission within two secondary schools in England and test for associations between self-reported social contacts, test positivity and the bacterial strain collected from the same students. Students filled out a social contact survey and their colonization status was ascertained through self-administered swabs from which isolates were sequenced. Isolates from the local community were also sequenced to assess the representativeness of school isolates. A low frequency of genome-linked transmission precluded a formal analysis of links between genomic and social networks, suggesting that transmission within schools is too rare to make it a viable tool for this purpose. Whilst we found no evidence that schools are an important route of transmission, increased colonization rates found within schools imply that school-age children may be an important source of community transmission.

Funding
This study was supported by the:
  • Wellcome Trust (Award 211864/Z/18/Z)
    • Principle Award Recipient: NotApplicable
  • Royal Society (Award 206250/Z/17/Z)
    • Principle Award Recipient: AndrewConlan
  • Evelyn Trust (Award 17/14)
    • Principle Award Recipient: AndrewConlan
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000993
2023-04-19
2024-04-18
Loading full text...

Full text loading...

/deliver/fulltext/mgen/9/4/mgen000993.html?itemId=/content/journal/mgen/10.1099/mgen.0.000993&mimeType=html&fmt=ahah

References

  1. Fine PEM, Clarkson JA. Measles in England and Wales – I: an analysis of factors underlying seasonal patterns. Int J Epidemiol 1982; 11:5–14 [View Article] [PubMed]
    [Google Scholar]
  2. Earn DJD, Rohani P, Bolker BM, Grenfell BT. A simple model for complex dynamical transitions in epidemics. Science 2000; 287:667–670 [View Article] [PubMed]
    [Google Scholar]
  3. Cauchemez S, Valleron AJ, Boëlle PY, Flahault A, Ferguson NM. Estimating the impact of school closure on influenza transmission from Sentinel data. Nature 2008; 452:750–754 [View Article] [PubMed]
    [Google Scholar]
  4. Cohen C, Kleynhans J, von Gottberg A, McMorrow ML, Wolter N et al. SARS-CoV-2 incidence, transmission and reinfection in a rural and an urban setting: results of the PHIRST-C cohort study, South Africa, 2020–21. Lancet Infect Dis 2021; 22:821–834 [View Article] [PubMed]
    [Google Scholar]
  5. Baguelin M, Flasche S, Camacho A, Demiris N, Miller E et al. Assessing optimal target populations for influenza vaccination programmes: an evidence synthesis and modelling study. PLoS Med 2013; 10:e1001527 [View Article] [PubMed]
    [Google Scholar]
  6. Mossong J, Hens N, Jit M, Beutels P, Auranen K et al. Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Med 2008; 5:e74 [View Article] [PubMed]
    [Google Scholar]
  7. Klepac P, Kucharski AJ, Conlan AJ, Kissler S, Tang ML et al. Contacts in context: large-scale setting-specific social mixing matrices from the BBC Pandemic project. medRxiv 20222020.02.16.20023754 [View Article]
    [Google Scholar]
  8. Jarvis CI, Van Zandvoort K, Gimma A, Prem K, Klepac P et al. Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK. BMC Med 2020; 18:124 [View Article] [PubMed]
    [Google Scholar]
  9. Harrison EM, Ludden C, Brodrick HJ, Blane B, Brennan G et al. Transmission of methicillin-resistant Staphylococcus aureus in long-term care facilities and their related healthcare networks. Genome Med 2016; 8:102 [View Article] [PubMed]
    [Google Scholar]
  10. Didelot X, Fraser C, Gardy J, Colijn C. Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks. Mol Biol Evol 2017; 34:997–1007 [View Article] [PubMed]
    [Google Scholar]
  11. Tong SYC, Davis JS, Eichenberger E, Holland TL, Fowler VG. Staphylococcus aureus infections: epidemiology, pathophysiology, clinical manifestations, and management. Clin Microbiol Rev 2015; 28:603–661 [View Article] [PubMed]
    [Google Scholar]
  12. Enright MC, Robinson DA, Randle G, Feil EJ, Grundmann H et al. The evolutionary history of methicillin-resistant Staphylococcus aureus (MRSA). Proc Natl Acad Sci 2002; 99:7687–7692 [View Article] [PubMed]
    [Google Scholar]
  13. Gamblin J, Jefferies JM, Harris S, Ahmad N, Marsh P et al. Nasal self-swabbing for estimating the prevalence of Staphylococcus aureus in the community. J Med Microbiol 2013; 62:437–440 [View Article] [PubMed]
    [Google Scholar]
  14. Miller RR, Walker AS, Godwin H, Fung R, Votintseva A et al. Dynamics of acquisition and loss of carriage of Staphylococcus aureus strains in the community: the effect of clonal complex. J Infect 2014; 68:426–439 [View Article] [PubMed]
    [Google Scholar]
  15. Wertheim HFL, Melles DC, Vos MC, van Leeuwen W, van Belkum A et al. The role of nasal carriage in Staphylococcus aureus infections. Lancet Infect Dis 2005; 5:751–762 [View Article] [PubMed]
    [Google Scholar]
  16. Grenfell BT, Pybus OG, Gog JR, Wood JLN, Daly JM et al. Unifying the epidemiological and evolutionary dynamics of pathogens. Science 2004; 303:327–332 [View Article] [PubMed]
    [Google Scholar]
  17. Hall MD, Woolhouse MEJ, Rambaut A. Using genomics data to reconstruct transmission trees during disease outbreaks. Rev Sci Tech 2016; 35:287–296 [View Article] [PubMed]
    [Google Scholar]
  18. Volz EM, Frost SDW. Inferring the source of transmission with phylogenetic data. PLoS Comput Biol 2013; 9:e1003397 [View Article] [PubMed]
    [Google Scholar]
  19. Gilbertson MLJ, Fountain-Jones NM, Craft ME. Incorporating genomic methods into contact networks to reveal new insights into animal behavior and infectious disease dynamics. Behaviour 2018; 155:759–791 [View Article] [PubMed]
    [Google Scholar]
  20. Grantz KH, Cummings DAT, Zimmer S, Vukotich C, Galloway D et al. Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys. Sci Rep 2021; 11:2319 [View Article]
    [Google Scholar]
  21. Aggarwal D, Warne B, Jahun AS, Hamilton WL, Fieldman T et al. Genomic epidemiology of SARS-CoV-2 in a UK university identifies dynamics of transmission. Nat Commun 2022; 13:751 [View Article] [PubMed]
    [Google Scholar]
  22. Harrison EM, Gleadall NS, Ba X, Danesh J, Peacock SJ et al. Validation of self-administered nasal swabs and postage for the isolation of Staphylococcus aureus. J Med Microbiol 2016; 65:1434–1437 [View Article] [PubMed]
    [Google Scholar]
  23. Conlan AJK, Eames KTD, Gage JA, von Kirchbach JC, Ross JV et al. Measuring social networks in British primary schools through scientific engagement. Proc R Soc B 2011; 278:1467–1475 [View Article]
    [Google Scholar]
  24. Eames KTD, Gage JA, Conlan AJK, Kucharski AJ, Gog JR. Opening the researcher’s world to school students. Math Sch 2013; 42:7–10
    [Google Scholar]
  25. Kucharski AJ, Conlan AJK, Eames KTD. School’s out: seasonal variation in the movement patterns of school children. PLoS One 2015; 10:e0128070 [View Article] [PubMed]
    [Google Scholar]
  26. Kucharski AJ, Wenham C, Brownlee P, Racon L, Widmer N et al. Structure and consistency of self-reported social contact networks in British secondary schools. PLoS One 2018; 13:e0200090 [View Article] [PubMed]
    [Google Scholar]
  27. Paterson GK, Larsen AR, Robb A, Edwards GE, Pennycott TW et al. The newly described mecA homologue, mecALGA251, is present in methicillin-resistant Staphylococcus aureus isolates from a diverse range of host species. J Antimicrob Chemother 2012; 67:2809–2813 [View Article] [PubMed]
    [Google Scholar]
  28. Loo M. The stringdist package for approximate string matching. R Journal 2014; 6:111–122 [View Article]
    [Google Scholar]
  29. R Core Team R: a Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing; 2021 www.R-project.org/
  30. Devleesschauwer B, Torgerson P, Charlier J, Levecke B, Praet N et al. prevalence: Tools for Prevalence Assessment Studies; 2015 http://cran.r-project.org/package=prevalence
  31. Bursac Z, Gauss CH, Williams DK, Hosmer DW. Purposeful selection of variables in logistic regression. Source Code Biol Med 2008; 3:17 [View Article] [PubMed]
    [Google Scholar]
  32. Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal, Complex Systems 20061695
    [Google Scholar]
  33. Pedersen TL. tidygraph: a Tidy API for Graph Manipulation; 2020 https://CRAN.R-project.org/package=tidygraph
  34. Pedersen TL. ggraph: an Implementation of Grammar of Graphics for Graphs and Networks; 2021 https://CRAN.R-project.org/package=ggraph
  35. Ford J. Social Class and the Comprehensive School London: Routledge and Kegan Paul; 1969
    [Google Scholar]
  36. Pons P, Latapy M. Computing communities in large networks using random walks (long version). arXiv:physics/0512106; 2005 http://arxiv.org/abs/physics/0512106 accessed 6 December 2021
  37. Newman MEJ. Assortative mixing in networks. Phys Rev Lett 2002; 89:208701 [View Article] [PubMed]
    [Google Scholar]
  38. Babraham Bioinformatics FastQC: a Quality Control Tool for High Throughput Sequence Data; 2021 www.bioinformatics.babraham.ac.uk/projects/fastqc/ accessed 14 December 2021
  39. Wood DE, Salzberg SL. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 2014; 15:R46 [View Article] [PubMed]
    [Google Scholar]
  40. Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: estimating species abundance in metagenomics data. PeerJ Comput Sci 2017; 3:e104 [View Article]
    [Google Scholar]
  41. Page AJ, De Silva N, Hunt M, Quail MA, Parkhill J et al. Robust high-throughput prokaryote de novo assembly and improvement pipeline for Illumina data. Microb Genom 2016; 2:e000083 [View Article] [PubMed]
    [Google Scholar]
  42. Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 2008; 18:821–829 [View Article] [PubMed]
    [Google Scholar]
  43. Boetzer M, Henkel CV, Jansen HJ, Butler D, Pirovano W. Scaffolding pre-assembled contigs using SSPACE. Bioinformatics 2011; 27:578–579 [View Article] [PubMed]
    [Google Scholar]
  44. Boetzer M, Pirovano W. Toward almost closed genomes with GapFiller. Genome Biol 2012; 13:R56 [View Article] [PubMed]
    [Google Scholar]
  45. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014; 30:2068–2069 [View Article] [PubMed]
    [Google Scholar]
  46. Hunt M, Mather AE, Sánchez-Busó L, Page AJ, Parkhill J et al. ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads. Microb Genom 2017; 3:e000131 [View Article] [PubMed]
    [Google Scholar]
  47. Tonkin-Hill G, MacAlasdair N, Ruis C, Weimann A, Horesh G et al. Producing polished prokaryotic pangenomes with the Panaroo pipeline. Genome Biol 2020; 21:180 [View Article] [PubMed]
    [Google Scholar]
  48. Page AJ, Taylor B, Delaney AJ, Soares J, Seemann T et al. SNP-sites: rapid efficient extraction of SNPs from multi-FASTA alignments. Microb Genom 2016; 2:e000056 [View Article] [PubMed]
    [Google Scholar]
  49. Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol 2015; 32:268–274 [View Article] [PubMed]
    [Google Scholar]
  50. Tonkin-Hill G. pairsnp; 2021 https://github.com/gtonkinhill/pairsnp accessed 14 December 2021
  51. Office for National Statistics COVID-19 Schools Infection Survey Newport: Office for National Statistics; 2022
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000993
Loading
/content/journal/mgen/10.1099/mgen.0.000993
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF

Supplementary material 2

EXCEL
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error