IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-42612-9.html
   My bibliography  Save this article

The counterintuitive implications of superspreading diseases

Author

Listed:
  • Bjarke Frost Nielsen

    (Roskilde University
    Princeton University)

  • Kim Sneppen

    (University of Copenhagen)

  • Lone Simonsen

    (Roskilde University)

Abstract

Superspreading is known to have played an important role in the transmission dynamics of SARS-CoV-2. In this Comment, the authors discuss how knowledge of the extent and cause of superspreading is important for designing appropriate control measures for emerging infectious diseases.

Suggested Citation

  • Bjarke Frost Nielsen & Kim Sneppen & Lone Simonsen, 2023. "The counterintuitive implications of superspreading diseases," Nature Communications, Nature, vol. 14(1), pages 1-3, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42612-9
    DOI: 10.1038/s41467-023-42612-9
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-42612-9
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-42612-9?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. 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. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Maarten Jan Wensink & Linda Juel Ahrenfeldt & Sören Möller, 2020. "Variability Matters," IJERPH, MDPI, vol. 18(1), pages 1-8, December.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Yunhwan Kim & Hohyung Ryu & Sunmi Lee, 2018. "Agent-Based Modeling for Super-Spreading Events: A Case Study of MERS-CoV Transmission Dynamics in the Republic of Korea," IJERPH, MDPI, vol. 15(11), pages 1-17, October.
    14. Anna C Peterson & Valerie J McKenzie, 2014. "Investigating Differences across Host Species and Scales to Explain the Distribution of the Amphibian Pathogen Batrachochytrium dendrobatidis," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-15, September.
    15. Wang, Jia-Zeng & Peng, Wei-Hua, 2020. "Fluctuations for the outbreak prevalence of the SIR epidemics in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    16. Lawrence M. Wein & Michael P. Atkinson, 2009. "Assessing Infection Control Measures for Pandemic Influenza," Risk Analysis, John Wiley & Sons, vol. 29(7), pages 949-962, July.
    17. Yong Sul Won & Jong-Hoon Kim & Chi Young Ahn & Hyojung Lee, 2021. "Subcritical Transmission in the Early Stage of COVID-19 in Korea," IJERPH, MDPI, vol. 18(3), pages 1-10, January.
    18. Alexandra Smirnova & Linda DeCamp & Gerardo Chowell, 2021. "Mathematical and Statistical Analysis of Doubling Times to Investigate the Early Spread of Epidemics: Application to the COVID-19 Pandemic," Mathematics, MDPI, vol. 9(6), pages 1-10, March.
    19. Calvin Pozderac & Brian Skinner, 2021. "Superspreading of SARS-CoV-2 in the USA," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-10, March.
    20. Wenting Yang & Jiantong Zhang & Ruolin Ma, 2020. "The Prediction of Infectious Diseases: A Bibliometric Analysis," IJERPH, MDPI, vol. 17(17), pages 1-19, August.

    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:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42612-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.