IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1007687.html
   My bibliography  Save this article

A model of tuberculosis clustering in low incidence countries reveals more transmission in the United Kingdom than the Netherlands between 2010 and 2015

Author

Listed:
  • Ellen Brooks-Pollock
  • Leon Danon
  • Hester Korthals Altes
  • Jennifer A Davidson
  • Andrew M T Pollock
  • Dick van Soolingen
  • Colin Campbell
  • Maeve K Lalor

Abstract

Tuberculosis (TB) remains a public health threat in low TB incidence countries, through a combination of reactivated disease and onward transmission. Using surveillance data from the United Kingdom (UK) and the Netherlands (NL), we demonstrate a simple and predictable relationship between the probability of observing a cluster and its size (the number of cases with a single genotype). We demonstrate that the full range of observed cluster sizes can be described using a modified branching process model with the individual reproduction number following a Poisson lognormal distribution. We estimate that, on average, between 2010 and 2015, a TB case generated 0.41 (95% CrI 0.30,0.60) secondary cases in the UK, and 0.24 (0.14,0.48) secondary cases in the NL. A majority of cases did not generate any secondary cases. Recent transmission accounted for 39% (26%,60%) of UK cases and 23%(13%,37%) of NL cases. We predict that reducing UK transmission rates to those observed in the NL would result in 538(266,818) fewer cases annually in the UK. In conclusion, while TB in low incidence countries is strongly associated with reactivated infections, we demonstrate that recent transmission remains sufficient to warrant policies aimed at limiting local TB spread.Author summary: Multiple tuberculosis (TB) cases infected with a single strain are known as a TB cluster. In the United Kingdom (UK) for example, TB clusters vary in size from two cases up to over 200 cases. Previous work on cluster sizes demonstrated that highly infectious individuals influence cluster size, but the analysis did not include the largest clusters. Here, we show that the chance of observing a cluster of a given size follows the same pattern in the UK and the NL. Using a new mathematical description of how clusters are formed, we are able to predict the chance of observing the full range of cluster sizes. Using the model, we estimate how many cases are due to recent transmission and how many other cases each case generates. Although we estimate that a minority of cases (39% (26%,60%) in the UK) are due to recent in-country transmission, we find that reducing the onward transmission in the UK to levels in the NL would result in 538 (266,818) fewer cases annually in the UK.

Suggested Citation

  • Ellen Brooks-Pollock & Leon Danon & Hester Korthals Altes & Jennifer A Davidson & Andrew M T Pollock & Dick van Soolingen & Colin Campbell & Maeve K Lalor, 2020. "A model of tuberculosis clustering in low incidence countries reveals more transmission in the United Kingdom than the Netherlands between 2010 and 2015," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-14, March.
    • Handle: RePEc:plo:pcbi00:1007687
    DOI: 10.1371/journal.pcbi.1007687
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007687
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007687&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1007687?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ellen Brooks-Pollock & Gareth O. Roberts & Matt J. Keeling, 2014. "A dynamic model of bovine tuberculosis spread and control in Great Britain," Nature, Nature, vol. 511(7508), pages 228-231, July.
    2. Gillespie, Colin S., 2015. "Fitting Heavy Tailed Distributions: The poweRlaw Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i02).
    3. J. O. Lloyd-Smith & S. J. Schreiber & P. E. Kopp & W. M. Getz, 2005. "Superspreading and the effect of individual variation on disease emergence," Nature, Nature, vol. 438(7066), pages 355-359, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lambert, Sébastien & Gilot-Fromont, Emmanuelle & Toïgo, Carole & Marchand, Pascal & Petit, Elodie & Garin-Bastuji, Bruno & Gauthier, Dominique & Gaillard, Jean-Michel & Rossi, Sophie & Thébault, Anne, 2020. "An individual-based model to assess the spatial and individual heterogeneity of Brucella melitensis transmission in Alpine ibex," Ecological Modelling, Elsevier, vol. 425(C).
    2. Tardy, Olivia & Lenglos, Christophe & Lai, Sandra & Berteaux, Dominique & Leighton, Patrick A., 2023. "Rabies transmission in the Arctic: An agent-based model reveals the effects of broad-scale movement strategies on contact risk between Arctic foxes," Ecological Modelling, Elsevier, vol. 476(C).
    3. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    4. Luc E. Coffeng & Sake J. de Vlas, 2022. "Predicting epidemics and the impact of interventions in heterogeneous settings: Standard SEIR models are too pessimistic," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 28-35, November.
    5. Joseph B. Bak-Coleman & Ian Kennedy & Morgan Wack & Andrew Beers & Joseph S. Schafer & Emma S. Spiro & Kate Starbird & Jevin D. West, 2022. "Combining interventions to reduce the spread of viral misinformation," Nature Human Behaviour, Nature, vol. 6(10), pages 1372-1380, October.
    6. Kris V. Parag & Robin N. Thompson & Christl A. Donnelly, 2022. "Are epidemic growth rates more informative than reproduction numbers?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 5-15, November.
    7. Thomas Ash & Antonio M. Bento & Daniel Kaffine & Akhil Rao & Ana I. Bento, 2022. "Disease-economy trade-offs under alternative epidemic control strategies," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    8. Maarten Jan Wensink & Linda Juel Ahrenfeldt & Sören Möller, 2020. "Variability Matters," IJERPH, MDPI, vol. 18(1), pages 1-8, December.
    9. Cura, Robin & Cottineau, Clémentine & Swerts, Elfie & Ignazzi, Cosmo Antonio & Bretagnolle, Anne & Vacchiani-Marcuzzo, Celine & Pumain, Denise, 2017. "The Old and the New: Qualifying City Systems in the World with Classical Models and New Data," SocArXiv pbzn6_v1, Center for Open Science.
    10. Katahira, Kei & Chen, Yu & Akiyama, Eizo, 2021. "Self-organized Speculation Game for the spontaneous emergence of financial stylized facts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    11. Lingcai Kong & Jinfeng Wang & Weiguo Han & Zhidong Cao, 2016. "Modeling Heterogeneity in Direct Infectious Disease Transmission in a Compartmental Model," IJERPH, MDPI, vol. 13(3), pages 1-13, February.
    12. Carolyn Ingram & Vicky Downey & Mark Roe & Yanbing Chen & Mary Archibald & Kadri-Ann Kallas & Jaspal Kumar & Peter Naughton & Cyril Onwuelazu Uteh & Alejandro Rojas-Chaves & Shibu Shrestha & Shiraz Sy, 2021. "COVID-19 Prevention and Control Measures in Workplace Settings: A Rapid Review and Meta-Analysis," IJERPH, MDPI, vol. 18(15), pages 1-26, July.
    13. Cura, Robin & Cottineau, Clémentine & Swerts, Elfie & Ignazzi, Cosmo Antonio & Bretagnolle, Anne & Vacchiani-Marcuzzo, Celine & Pumain, Denise, 2017. "The Old and the New: Qualifying City Systems in the World with Classical Models and New Data," SocArXiv pbzn6, Center for Open Science.
    14. Robin N Thompson & Christopher A Gilligan & Nik J Cunniffe, 2016. "Detecting Presymptomatic Infection Is Necessary to Forecast Major Epidemics in the Earliest Stages of Infectious Disease Outbreaks," PLOS Computational Biology, Public Library of Science, vol. 12(4), pages 1-18, April.
    15. repec:wsi:acsxxx:v:21:y:2018:i:08:n:s0219525918500194 is not listed on IDEAS
    16. Tiandong Wang & Sidney Resnick, 2023. "Poisson Edge Growth and Preferential Attachment Networks," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-25, March.
    17. Zengmiao Wang & Peng Yang & Ruixue Wang & Luca Ferretti & Lele Zhao & Shan Pei & Xiaoli Wang & Lei Jia & Daitao Zhang & Yonghong Liu & Ziyan Liu & Quanyi Wang & Christophe Fraser & Huaiyu Tian, 2024. "Estimating the contribution of setting-specific contacts to SARS-CoV-2 transmission using digital contact tracing data," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    18. Xue Cui & Lu Yang, 2024. "Systemic risk and idiosyncratic networks among global systemically important banks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 58-75, January.
    19. Kei Katahira & Yu Chen, 2019. "Heterogeneous wealth distribution, round-trip trading and the emergence of volatility clustering in Speculation Game," Papers 1909.03185, arXiv.org.
    20. T Alex Perkins & Thomas W Scott & Arnaud Le Menach & David L Smith, 2013. "Heterogeneity, Mixing, and the Spatial Scales of Mosquito-Borne Pathogen Transmission," PLOS Computational Biology, Public Library of Science, vol. 9(12), pages 1-16, December.
    21. Nathan O. Hodas & Jacob Hunter & Stephen J. Young & Kristina Lerman, 2018. "Model of cognitive dynamics predicts performance on standardized tests," Journal of Computational Social Science, Springer, vol. 1(2), pages 295-312, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1007687. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.