IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0259097.html
   My bibliography  Save this article

High variability in transmission of SARS-CoV-2 within households and implications for control

Author

Listed:
  • Damon J A Toth
  • Alexander B Beams
  • Lindsay T Keegan
  • Yue Zhang
  • Tom Greene
  • Brian Orleans
  • Nathan Seegert
  • Adam Looney
  • Stephen C Alder
  • Matthew H Samore

Abstract

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a high risk of transmission in close-contact indoor settings, which may include households. Prior studies have found a wide range of household secondary attack rates and may contain biases due to simplifying assumptions about transmission variability and test accuracy. Methods: We compiled serological SARS-CoV-2 antibody test data and prior SARS-CoV-2 test reporting from members of 9,224 Utah households. We paired these data with a probabilistic model of household importation and transmission. We calculated a maximum likelihood estimate of the importation probability, mean and variability of household transmission probability, and sensitivity and specificity of test data. Given our household transmission estimates, we estimated the threshold of non-household transmission required for epidemic growth in the population. Results: We estimated that individuals in our study households had a 0.41% (95% CI 0.32%– 0.51%) chance of acquiring SARS-CoV-2 infection outside their household. Our household secondary attack rate estimate was 36% (27%– 48%), substantially higher than the crude estimate of 16% unadjusted for imperfect serological test specificity and other factors. We found evidence for high variability in individual transmissibility, with higher probability of no transmissions or many transmissions compared to standard models. With household transmission at our estimates, the average number of non-household transmissions per case must be kept below 0.41 (0.33–0.52) to avoid continued growth of the pandemic in Utah. Conclusions: Our findings suggest that crude estimates of household secondary attack rate based on serology data without accounting for false positive tests may underestimate the true average transmissibility, even when test specificity is high. Our finding of potential high variability (overdispersion) in transmissibility of infected individuals is consistent with characterizing SARS-CoV-2 transmission being largely driven by superspreading from a minority of infected individuals. Mitigation efforts targeting large households and other locations where many people congregate indoors might curb continued spread of the virus.

Suggested Citation

  • Damon J A Toth & Alexander B Beams & Lindsay T Keegan & Yue Zhang & Tom Greene & Brian Orleans & Nathan Seegert & Adam Looney & Stephen C Alder & Matthew H Samore, 2021. "High variability in transmission of SARS-CoV-2 within households and implications for control," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-21, November.
  • Handle: RePEc:plo:pone00:0259097
    DOI: 10.1371/journal.pone.0259097
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0259097
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0259097&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0259097?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.
    2. Yunjun Zhang & Yuying Li & Lu Wang & Mingyuan Li & Xiaohua Zhou, 2020. "Evaluating Transmission Heterogeneity and Super-Spreading Event of COVID-19 in a Metropolis of China," IJERPH, MDPI, vol. 17(10), pages 1-11, May.
    3. Liang Wang & Xavier Didelot & Jing Yang & Gary Wong & Yi Shi & Wenjun Liu & George F. Gao & Yuhai Bi, 2020. "Inference of person-to-person transmission of COVID-19 reveals hidden super-spreading events during the early outbreak phase," Nature Communications, Nature, vol. 11(1), pages 1-6, December.
    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. Elías, L. Llamazares & Elías, S. Llamazares & del Rey, A. Martín, 2022. "An analysis of contact tracing protocol in an over-dispersed SEIQR Covid-like disease," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(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. Arnab K Ghosh & Sara Venkatraman & Evgeniya Reshetnyak & Mangala Rajan & Anjile An & John K Chae & Mark A Unruh & David Abramson & Charles DiMaggio & Nathaniel Hupert, 2022. "Association between city-wide lockdown and COVID-19 hospitalization rates in multigenerational households in New York City," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-13, March.
    4. 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.
    5. Moshe B Hoshen & Anthony H Burton & Themis J V Bowcock, 2007. "Simulating disease transmission dynamics at a multi-scale level," International Journal of Microsimulation, International Microsimulation Association, vol. 1(1), pages 26-34.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Maarten Jan Wensink & Linda Juel Ahrenfeldt & Sören Möller, 2020. "Variability Matters," IJERPH, MDPI, vol. 18(1), pages 1-8, December.
    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. Wayne M. Getz & Jean-Paul Gonzalez & Richard Salter & James Bangura & Colin Carlson & Moinya Coomber & Eric Dougherty & David Kargbo & Nathan D. Wolfe & Nadia Wauquier, 2015. "Tactics and Strategies for Managing Ebola Outbreaks and the Salience of Immunization," Post-Print hal-01214432, HAL.
    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. Kathrin Büttner & Joachim Krieter & Arne Traulsen & Imke Traulsen, 2013. "Efficient Interruption of Infection Chains by Targeted Removal of Central Holdings in an Animal Trade Network," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-12, September.
    16. Jonas Dehning & Sebastian B. Mohr & Sebastian Contreras & Philipp Dönges & Emil N. Iftekhar & Oliver Schulz & Philip Bechtle & Viola Priesemann, 2023. "Impact of the Euro 2020 championship on the spread of COVID-19," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    17. 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.
    18. Jonas I Liechti & Gabriel E Leventhal & Sebastian Bonhoeffer, 2017. "Host population structure impedes reversion to drug sensitivity after discontinuation of treatment," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-19, August.
    19. 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.
    20. Otilia Boldea & Adriana Cornea-Madeira & João Madeira, 2023. "Disentangling the effect of measures, variants, and vaccines on SARS-CoV-2 infections in England: a dynamic intensity model," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 444-466.

    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:pone00:0259097. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.