IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-39638-4.html
   My bibliography  Save this article

Swift and extensive Omicron outbreak in China after sudden exit from ‘zero-COVID’ policy

Author

Listed:
  • Emma E. Goldberg

    (Los Alamos National Laboratory)

  • Qianying Lin

    (Los Alamos National Laboratory)

  • Ethan O. Romero-Severson

    (Los Alamos National Laboratory)

  • Ruian Ke

    (Los Alamos National Laboratory)

Abstract

In late 2022, China transitioned from a strict ‘zero-COVID’ policy to rapidly abandoning nearly all interventions and data reporting. This raised great concern about the presumably-rapid but unreported spread of the SARS-CoV-2 Omicron variant in a very large population of very low pre-existing immunity. By modeling a combination of case count and survey data, we show that Omicron spread extremely rapidly, at a rate of 0.42/day (95% credibility interval: [0.35, 0.51]/day), translating to an epidemic doubling time of 1.6 days ([1.6, 2.0] days) after the full exit from zero-COVID on Dec. 7, 2022. Consequently, we estimate that the vast majority of the population (97% [95%, 99%], sensitivity analysis lower limit of 90%) was infected during December, with the nation-wide epidemic peaking on Dec. 23. Overall, our results highlight the extremely high transmissibility of the variant and the importance of proper design of intervention exit strategies to avoid large infection waves.

Suggested Citation

  • Emma E. Goldberg & Qianying Lin & Ethan O. Romero-Severson & Ruian Ke, 2023. "Swift and extensive Omicron outbreak in China after sudden exit from ‘zero-COVID’ policy," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39638-4
    DOI: 10.1038/s41467-023-39638-4
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-39638-4
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-39638-4?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. Helen J Wearing & Pejman Rohani & Matt J Keeling, 2005. "Appropriate Models for the Management of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 2(7), pages 1-1, July.
    2. Pierre Nouvellet & Sangeeta Bhatia & Anne Cori & Kylie E. C. Ainslie & Marc Baguelin & Samir Bhatt & Adhiratha Boonyasiri & Nicholas F. Brazeau & Lorenzo Cattarino & Laura V. Cooper & Helen Coupland &, 2021. "Reduction in mobility and COVID-19 transmission," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jing Wei & Zhanqing Li & Alexei Lyapustin & Jun Wang & Oleg Dubovik & Joel Schwartz & Lin Sun & Chi Li & Song Liu & Tong Zhu, 2023. "First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

    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. Lahrouz, A. & El Mahjour, H. & Settati, A. & Bernoussi, A., 2018. "Dynamics and optimal control of a non-linear epidemic model with relapse and cure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 299-317.
    2. Richard C. Larson, 2007. "Simple Models of Influenza Progression Within a Heterogeneous Population," Operations Research, INFORMS, vol. 55(3), pages 399-412, June.
    3. 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.
    4. Victoria Chebotaeva & Paula A. Vasquez, 2023. "Erlang-Distributed SEIR Epidemic Models with Cross-Diffusion," Mathematics, MDPI, vol. 11(9), pages 1-18, May.
    5. Daniele Proverbio & Françoise Kemp & Stefano Magni & Andreas Husch & Atte Aalto & Laurent Mombaerts & Alexander Skupin & Jorge Gonçalves & Jose Ameijeiras-Alonso & Christophe Ley, 2021. "Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-21, May.
    6. Meng, Xin & Guo, Mingxue & Gao, Ziyou & Yang, Zhenzhen & Yuan, Zhilu & Kang, Liujiang, 2022. "The effects of Wuhan highway lockdown measures on the spread of COVID-19 in China," Transport Policy, Elsevier, vol. 117(C), pages 169-180.
    7. Haque, Md Tabish & Hamid, Faiz, 2022. "An optimization model to assign seats in long distance trains to minimize SARS-CoV-2 diffusion," Transportation Research Part A: Policy and Practice, Elsevier, vol. 162(C), pages 104-120.
    8. Bekiros, Stelios & Kouloumpou, Dimitra, 2020. "SBDiEM: A new mathematical model of infectious disease dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    9. Carlos Díaz & Sebastian Fossati & Nicolás Trajtenberg, 2022. "Stay at home if you can: COVID‐19 stay‐at‐home guidelines and local crime," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 19(4), pages 1067-1113, December.
    10. Carbone, Giuseppe & De Vincenzo, Ilario, 2022. "A general theory for infectious disease dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    11. Heinlein, Bastian & De Domenico, Manlio, 2023. "Unraveling the role of adapting risk perception during the COVID-19 pandemic in Europe," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    12. Peace, Angela & O’Regan, Suzanne M. & Spatz, Jennifer A. & Reilly, Patrick N. & Hill, Rachel D. & Carter, E. Davis & Wilkes, Rebecca P. & Waltzek, Thomas B. & Miller, Debra L. & Gray, Matthew J., 2019. "A highly invasive chimeric ranavirus can decimate tadpole populations rapidly through multiple transmission pathways," Ecological Modelling, Elsevier, vol. 410(C), pages 1-1.
    13. Hornstein Andreas, 2022. "Quarantine, Contact Tracing, and Testing: Implications of an Augmented SEIR Model," The B.E. Journal of Macroeconomics, De Gruyter, vol. 22(1), pages 53-88, January.
    14. Miclo, Laurent & Spiro, Daniel & Weibull, Jörgen, 2022. "Optimal epidemic suppression under an ICU constraint: An analytical solution," Journal of Mathematical Economics, Elsevier, vol. 101(C).
    15. Michael A Johansson & Neysarí Arana-Vizcarrondo & Brad J Biggerstaff & J Erin Staples & Nancy Gallagher & Nina Marano, 2011. "On the Treatment of Airline Travelers in Mathematical Models," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-7, July.
    16. Burcu Ozgun & Tom Broekel, 2024. "Saved by the news? COVID-19 in German news and its relationship with regional mobility behaviour," Regional Studies, Taylor & Francis Journals, vol. 58(2), pages 365-380, February.
    17. Li, Meng-Hao & Haynes, Kingsley & Kulkarni, Rajendra & Siddique, Abu Bakkar, 2022. "Determinants of voluntary compliance: COVID-19 mitigation," Social Science & Medicine, Elsevier, vol. 310(C).
    18. Liu, Shasha & Yamamoto, Toshiyuki, 2022. "Role of stay-at-home requests and travel restrictions in preventing the spread of COVID-19 in Japan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 1-16.
    19. Annemarie Bouma & Ivo Claassen & Ketut Natih & Don Klinkenberg & Christl A Donnelly & Guus Koch & Michiel van Boven, 2009. "Estimation of Transmission Parameters of H5N1 Avian Influenza Virus in Chickens," PLOS Pathogens, Public Library of Science, vol. 5(1), pages 1-13, January.
    20. Ross, J.V. & Pagendam, D.E. & Pollett, P.K., 2009. "On parameter estimation in population models II: Multi-dimensional processes and transient dynamics," Theoretical Population Biology, Elsevier, vol. 75(2), pages 123-132.

    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_s41467-023-39638-4. 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.