IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v11y2020i1d10.1038_s41467-020-19393-6.html
   My bibliography  Save this article

Modelling transmission and control of the COVID-19 pandemic in Australia

Author

Listed:
  • Sheryl L. Chang

    (University of Sydney)

  • Nathan Harding

    (University of Sydney)

  • Cameron Zachreson

    (University of Sydney)

  • Oliver M. Cliff

    (University of Sydney)

  • Mikhail Prokopenko

    (University of Sydney
    University of Sydney)

Abstract

There is a continuing debate on relative benefits of various mitigation and suppression strategies aimed to control the spread of COVID-19. Here we report the results of agent-based modelling using a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia. This model is calibrated to match key characteristics of COVID-19 transmission. An important calibration outcome is the age-dependent fraction of symptomatic cases, with this fraction for children found to be one-fifth of such fraction for adults. We apply the model to compare several intervention strategies, including restrictions on international air travel, case isolation, home quarantine, social distancing with varying levels of compliance, and school closures. School closures are not found to bring decisive benefits unless coupled with high level of social distancing compliance. We report several trade-offs, and an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels, with compliance at the 90% level found to control the disease within 13–14 weeks, when coupled with effective case isolation and international travel restrictions.

Suggested Citation

  • Sheryl L. Chang & Nathan Harding & Cameron Zachreson & Oliver M. Cliff & Mikhail Prokopenko, 2020. "Modelling transmission and control of the COVID-19 pandemic in Australia," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19393-6
    DOI: 10.1038/s41467-020-19393-6
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-020-19393-6
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-020-19393-6?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
    ---><---

    Citations

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


    Cited by:

    1. Marc Diederichs & Reyn van Ewijk & Ingo E. Isphording & Nico Pestel, 2022. "Schools under mandatory testing can mitigate the spread of SARS-CoV-2," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(26), pages 2201724119-, June.
    2. Meng, Xueyu & Han, Sijie & Wu, Leilei & Si, Shubin & Cai, Zhiqiang, 2022. "Analysis of epidemic vaccination strategies by node importance and evolutionary game on complex networks," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    3. Nathan H. Schumaker & Sydney M. Watkins, 2021. "Adding Space to Disease Models: A Case Study with COVID-19 in Oregon, USA," Land, MDPI, vol. 10(4), pages 1-13, April.
    4. Shailesh Bharati & Rahul Batra, 2021. "How Misuse of Statistics Can Spread Misinformation: A Study of Misrepresentation of COVID-19 Data," Papers 2102.07198, arXiv.org.
    5. Hafiz Suliman Munawar & Sara Imran Khan & Zakria Qadir & Yusra Sajid Kiani & Abbas Z. Kouzani & M. A. Parvez Mahmud, 2021. "Insights into the Mobility Pattern of Australians during COVID-19," Sustainability, MDPI, vol. 13(17), pages 1-19, August.
    6. Mimi E. Lam, 2021. "United by the global COVID-19 pandemic: divided by our values and viral identities," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-6, December.
    7. Leonardo López & Xavier Rodó, 2020. "The end of social confinement and COVID-19 re-emergence risk," Nature Human Behaviour, Nature, vol. 4(7), pages 746-755, July.
    8. Mohamed R Ibrahim & James Haworth & Aldo Lipani & Nilufer Aslam & Tao Cheng & Nicola Christie, 2021. "Variational-LSTM autoencoder to forecast the spread of coronavirus across the globe," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-22, January.
    9. Ahmad B. Hassanat & Sami Mnasri & Mohammed A. Aseeri & Khaled Alhazmi & Omar Cheikhrouhou & Ghada Altarawneh & Malek Alrashidi & Ahmad S. Tarawneh & Khalid S. Almohammadi & Hani Almoamari, 2021. "A Simulation Model for Forecasting COVID-19 Pandemic Spread: Analytical Results Based on the Current Saudi COVID-19 Data," Sustainability, MDPI, vol. 13(9), pages 1-22, April.
    10. Leonardo José Mataruna-Dos-Santos & Pedro da Gama Roberto de Albuquerque & Gabriel de Almeida Vasconcellos & Rodrigo Mendonça do Nascimento & Nadine Tonelli Cavalari & Daniel Range & Andressa Fontes G, 2021. "An Analysis Safe Protocols Employed in Professional Male Soccer and the Impacts of the COVID-19 Pandemic on the 2020 Brazilian Championship," Sustainability, MDPI, vol. 13(24), pages 1-16, December.
    11. Chen, Kexin & Pun, Chi Seng & Wong, Hoi Ying, 2023. "Efficient social distancing during the COVID-19 pandemic: Integrating economic and public health considerations," European Journal of Operational Research, Elsevier, vol. 304(1), pages 84-98.
    12. Zhang, Junyi & Zhang, Runsen & Ding, Hongxiang & Li, Shuangjin & Liu, Rui & Ma, Shuang & Zhai, Baoxin & Kashima, Saori & Hayashi, Yoshitsugu, 2021. "Effects of transport-related COVID-19 policy measures: A case study of six developed countries," Transport Policy, Elsevier, vol. 110(C), pages 37-57.
    13. Panarello, Demetrio & Tassinari, Giorgio, 2022. "One year of COVID-19 in Italy: are containment policies enough to shape the pandemic pattern?," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    14. Nagel, Kai & Rakow, Christian & Müller, Sebastian A., 2021. "Realistic agent-based simulation of infection dynamics and percolation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    15. Gregory L Watson & Di Xiong & Lu Zhang & Joseph A Zoller & John Shamshoian & Phillip Sundin & Teresa Bufford & Anne W Rimoin & Marc A Suchard & Christina M Ramirez, 2021. "Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-20, March.
    16. Choi, Youngran & Zou, Li & Dresner, Martin, 2022. "The effects of air transport mobility and global connectivity on viral transmission: Lessons learned from Covid-19 and its variants," Transport Policy, Elsevier, vol. 127(C), pages 22-30.
    17. Nguyen, Tri K. & Hoang, Nam H. & Currie, Graham & Vu, Hai L., 2022. "Enhancing Covid-19 virus spread modeling using an activity travel model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 161(C), pages 186-199.
    18. Bloise, Francesco & Tancioni, Massimiliano, 2021. "Predicting the spread of COVID-19 in Italy using machine learning: Do socio-economic factors matter?," Structural Change and Economic Dynamics, Elsevier, vol. 56(C), pages 310-329.
    19. Daniel K Sewell & Aaron Miller & for the CDC MInD-Healthcare Program, 2020. "Simulation-free estimation of an individual-based SEIR model for evaluating nonpharmaceutical interventions with an application to COVID-19 in the District of Columbia," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-18, November.
    20. Eckhard Platen, 2020. "Stochastic Modelling of the COVID-19 Epidemic," Research Paper Series 409, Quantitative Finance Research Centre, University of Technology, Sydney.
    21. Ma, Xiangyu & Zhou, Huijie & Li, Zhiyi, 2021. "On the resilience of modern power systems: A complex network perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    22. Tsiligianni, Christiana & Tsiligiannis, Aristeides & Tsiliyannis, Christos, 2023. "A stochastic inventory model of COVID-19 and robust, real-time identification of carriers at large and infection rate via asymptotic laws," European Journal of Operational Research, Elsevier, vol. 304(1), pages 42-56.
    23. Quang Dang Nguyen & Mikhail Prokopenko, 2022. "A general framework for optimising cost-effectiveness of pandemic response under partial intervention measures," Papers 2205.08996, arXiv.org, revised Nov 2022.
    24. Luis Vargas Tamayo & Vianney Mbazumutima & Christopher Thron & Léonard Todjihounde, 2021. "Three-Stage Numerical Solution for Optimal Control of COVID-19," Mathematics, MDPI, vol. 9(15), pages 1-26, July.

    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:11:y:2020:i:1:d:10.1038_s41467-020-19393-6. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.