IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v632y2023ip1s0378437123008853.html
   My bibliography  Save this article

Testing lockdown measures in epidemic outbreaks through mean-field models considering the social structure

Author

Listed:
  • Rozan, E.A.
  • Bouzat, S.
  • Kuperman, M.N.

Abstract

Lately, concepts such as lockdown, quarantine, and social distancing have become very relevant since they have been associated with essential measures in the prevention and mitigation of COVID-19. While some conclusions about the effectiveness of these measures could be drawn from field observations, many mathematical models aimed to provide some clues. However, the reliability of these models is questioned, especially if the social structure is not included in them. In this work, we propose a mesoscopic model that allows the evaluation of the effect of measures such as social distancing and lockdown when the social topology is taken into account. The model is able to predict successive waves of infections without the need to account for reinfections, and it can qualitatively reproduce the wave patterns observed across many countries during the COVID-19 pandemic. Subsequent waves can have a higher peak of infections if the restrictiveness of the lockdown is above a certain threshold. The model is flexible and can implement various social distancing strategies by adjusting the restrictiveness and the duration of lockdown measures or specifying whether they occur once or repeatedly. It also includes the option to consider essential workers that do not isolate during a lockdown.

Suggested Citation

  • Rozan, E.A. & Bouzat, S. & Kuperman, M.N., 2023. "Testing lockdown measures in epidemic outbreaks through mean-field models considering the social structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    • Handle: RePEc:eee:phsmap:v:632:y:2023:i:p1:s0378437123008853
    DOI: 10.1016/j.physa.2023.129330
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437123008853
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2023.129330?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. Cornes, F.E. & Frank, G.A. & Dorso, C.O., 2022. "COVID-19 spreading under containment actions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    2. Alexander Karaivanov, 2020. "A social network model of COVID-19," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-33, October.
    3. Reda Cherif & Fuad Hasanov, 2020. "A TIP Against the COVID-19 Pandemic," IMF Working Papers 2020/114, International Monetary Fund.
    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. James, Nick & Menzies, Max, 2022. "Global and regional changes in carbon dioxide emissions: 1970–2019," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    2. Andrew Atkeson & Michael C. Droste & Michael Mina & James H. Stock, 2020. "Economic Benefits of COVID-19 Screening Tests," NBER Working Papers 28031, National Bureau of Economic Research, Inc.
    3. Nick James & Max Menzies, 2021. "Efficiency of communities and financial markets during the 2020 pandemic," Papers 2104.02318, arXiv.org, revised Jul 2021.
    4. Thomas Gries & Paul J. J. Welfens, 2021. "Testing as an approach to control the Corona epidemic dynamics and avoid lockdowns," International Economics and Economic Policy, Springer, vol. 18(1), pages 1-24, February.
    5. Roland Pongou & Guy Tchuente & Jean-Baptiste Tondji, 2021. "Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States," Papers 2110.10230, arXiv.org.
    6. Bertogg, Ariane & Koos, Sebastian, 2021. "Changes of Social Networks during the Covid-19 Pandemic: Who is affected and what are its Consequences for Psychological Strain?," Working Papers 07, University of Konstanz, Cluster of Excellence "The Politics of Inequality. Perceptions, Participation and Policies".
    7. Roland Pongou & Guy Tchuente & Jean-Baptiste Tondji, 2023. "Optimal interventions in networks during a pandemic," Journal of Population Economics, Springer;European Society for Population Economics, vol. 36(2), pages 847-883, April.
    8. Mahapatra, D.P. & Triambak, S., 2022. "Towards predicting COVID-19 infection waves: A random-walk Monte Carlo simulation approach," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    9. L. S. Sanna Stephan, 2023. "A Trimming Estimator for the Latent-Diffusion-Observed-Adoption Model," Papers 2309.01471, arXiv.org.
    10. Panicker, Akhil & Sasidevan, V., 2024. "Social adaptive behavior and oscillatory prevalence in an epidemic model on evolving random geometric graphs," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    11. Marina Azzimonti-Renzo & Alessandra Fogli & Fabrizio Perri & Mark Ponder, 2020. "Pandemic Control in ECON-EPI Networks," Staff Report 609, Federal Reserve Bank of Minneapolis.
    12. James, Nick & Menzies, Max, 2023. "Collective infectivity of the pandemic over time and association with vaccine coverage and economic development," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    13. Casey B. Mulligan, 2021. "The Backward Art of Slowing the Spread? Congregation Efficiencies during COVID-19," NBER Working Papers 28737, National Bureau of Economic Research, Inc.
    14. Pongou, Roland & Tchuente, Guy & Tondji, Jean-Baptiste, 2021. "Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States," GLO Discussion Paper Series 957, Global Labor Organization (GLO).
    15. L. S. Sanna Stephan, 2023. "Moment-Based Estimation of Diffusion and Adoption Parameters in Networks," Papers 2309.01489, arXiv.org.
    16. Agarwal,Ruchir & Reed,Tristan, 2021. "How to End the COVID-19 Pandemic by March 2022," Policy Research Working Paper Series 9632, The World Bank.
    17. Eduardo Gutiérrez & Enrique Moral-Benito, 2020. "Medidas de contención, evolución del empleo y propagación del Covid-19 en los municipios españoles," Occasional Papers 2022, Banco de España.
    18. Jayles, Bertrand & Cheong, Siew Ann & Herrmann, Hans J., 2022. "Modeling the resilience of social networks to lockdowns regarding the dynamics of meetings," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).

    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:eee:phsmap:v:632:y:2023:i:p1:s0378437123008853. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.