IDEAS home Printed from https://ideas.repec.org/a/nat/nathum/v4y2020i7d10.1038_s41562-020-0908-8.html
   My bibliography  Save this article

The end of social confinement and COVID-19 re-emergence risk

Author

Listed:
  • Leonardo López

    (Barcelona Institute for Global Health)

  • Xavier Rodó

    (Barcelona Institute for Global Health
    ICREA)

Abstract

The lack of effective pharmaceutical interventions for SARS-CoV-2 raises the possibility of COVID-19 recurrence. We explore different post-confinement scenarios by using a stochastic modified SEIR (susceptible–exposed–infectious–recovered) model that accounts for the spread of infection during the latent period and also incorporates time-decaying effects due to potential loss of acquired immunity, people’s increasing awareness of social distancing and the use of non-pharmaceutical interventions. Our results suggest that lockdowns should remain in place for at least 60 days to prevent epidemic growth, as well as a potentially larger second wave of SARS-CoV-2 cases occurring within months. The best-case scenario should also gradually incorporate workers in a daily proportion at most 50% higher than during the confinement period. We show that decaying immunity and particularly awareness and behaviour have 99% significant effects on both the current wave of infection and on preventing COVID-19 re-emergence. Social distancing and individual non-pharmaceutical interventions could potentially remove the need for lockdowns.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:nathum:v:4:y:2020:i:7:d:10.1038_s41562-020-0908-8
    DOI: 10.1038/s41562-020-0908-8
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41562-020-0908-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/s41562-020-0908-8?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    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. Bienvenido-Huertas, David, 2021. "Do unemployment benefits and economic aids to pay electricity bills remove the energy poverty risk of Spanish family units during lockdown? A study of COVID-19-induced lockdown," Energy Policy, Elsevier, vol. 150(C).
    2. Simin Zou & Xuhui He, 2021. "Effect of Train-Induced Wind on the Transmission of COVID-19: A New Insight into Potential Infectious Risks," IJERPH, MDPI, vol. 18(15), pages 1-17, August.
    3. Naudé, Wim, 2023. "We Already Live in a Degrowth World, and We Do Not like It," IZA Discussion Papers 16191, Institute of Labor Economics (IZA).

    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. 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. Eckhard Platen, 2020. "Stochastic Modelling of the COVID-19 Epidemic," Research Paper Series 409, Quantitative Finance Research Centre, University of Technology, Sydney.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    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. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.

    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:nathum:v:4:y:2020:i:7:d:10.1038_s41562-020-0908-8. 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.