IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v11y2020i1d10.1038_s41467-020-18827-5.html
   My bibliography  Save this article

A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic

Author

Listed:
  • Fabio Della Rossa

    (Politecnico di Milano
    University of Naples Federico II)

  • Davide Salzano

    (University of Naples Federico II)

  • Anna Di Meglio

    (University of Naples Federico II)

  • Francesco De Lellis

    (University of Naples Federico II)

  • Marco Coraggio

    (University of Naples Federico II)

  • Carmela Calabrese

    (University of Naples Federico II)

  • Agostino Guarino

    (University of Naples Federico II)

  • Ricardo Cardona-Rivera

    (University of Naples Federico II)

  • Pietro De Lellis

    (University of Naples Federico II)

  • Davide Liuzza

    (Fusion and Nuclear Safety Department)

  • Francesco Lo Iudice

    (University of Naples Federico II)

  • Giovanni Russo

    (University of Salerno)

  • Mario di Bernardo

    (University of Naples Federico II)

Abstract

The COVID-19 epidemic hit Italy particularly hard, yielding the implementation of strict national lockdown rules. Previous modelling studies at the national level overlooked the fact that Italy is divided into administrative regions which can independently oversee their own share of the Italian National Health Service. Here, we show that heterogeneity between regions is essential to understand the spread of the epidemic and to design effective strategies to control the disease. We model Italy as a network of regions and parameterize the model of each region on real data spanning over two months from the initial outbreak. We confirm the effectiveness at the regional level of the national lockdown strategy and propose coordinated regional interventions to prevent future national lockdowns, while avoiding saturation of the regional health systems and mitigating impact on costs. Our study and methodology can be easily extended to other levels of granularity to support policy- and decision-makers.

Suggested Citation

  • Fabio Della Rossa & Davide Salzano & Anna Di Meglio & Francesco De Lellis & Marco Coraggio & Carmela Calabrese & Agostino Guarino & Ricardo Cardona-Rivera & Pietro De Lellis & Davide Liuzza & Francesc, 2020. "A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18827-5
    DOI: 10.1038/s41467-020-18827-5
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-020-18827-5
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-020-18827-5?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. Liang, Zhenglin & Jiang, Chen & Sun, Muxia & Xue, Zongqi & Li, Yan-Fu, 2023. "Resilience analysis for confronting the spreading risk of contagious diseases," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    2. Reyna-Lara, Adriana & Soriano-Paños, David & Arenas, Alex & Gómez-Gardeñes, Jesús, 2022. "The interconnection between independent reactive control policies drives the stringency of local containment," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    3. Gorji, Mohammad-Ali & Shetab-Boushehri, Seyyed-Nader & Akbarzadeh, Meisam, 2022. "Developing public transportation resilience against the epidemic through government tax policies: A game-theoretic approach," Transport Policy, Elsevier, vol. 128(C), pages 229-239.
    4. Luca Scrucca, 2022. "A COVINDEX based on a GAM beta regression model with an application to the COVID-19 pandemic in Italy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 881-900, October.
    5. Myladis R. Cogollo & Gilberto González-Parra & Abraham J. Arenas, 2021. "Modeling and Forecasting Cases of RSV Using Artificial Neural Networks," Mathematics, MDPI, vol. 9(22), pages 1-20, November.
    6. Francesco Piccialli & Vincenzo Schiano Cola & Fabio Giampaolo & Salvatore Cuomo, 2021. "The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic," Information Systems Frontiers, Springer, vol. 23(6), pages 1467-1497, December.
    7. Cristiano Maria Verrelli & Fabio Della Rossa, 2021. "Two-Age-Structured COVID-19 Epidemic Model: Estimation of Virulence Parameters to Interpret Effects of National and Regional Feedback Interventions and Vaccination," Mathematics, MDPI, vol. 9(19), pages 1-12, September.
    8. Michelangelo Bin & Peter Y K Cheung & Emanuele Crisostomi & Pietro Ferraro & Hugo Lhachemi & Roderick Murray-Smith & Connor Myant & Thomas Parisini & Robert Shorten & Sebastian Stein & Lewi Stone, 2021. "Post-lockdown abatement of COVID-19 by fast periodic switching," PLOS Computational Biology, Public Library of Science, vol. 17(1), pages 1-34, January.
    9. Mingolla, Stefano & Lu, Zhongming, 2022. "Impact of implementation timing on the effectiveness of stay-at-home requirement under the COVID-19 pandemic: Lessons from the Italian Case," Health Policy, Elsevier, vol. 126(6), pages 504-511.
    10. Buonomo, Bruno & Giacobbe, Andrea, 2023. "Oscillations in SIR behavioural epidemic models: The interplay between behaviour and overexposure to infection," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).

    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_s41467-020-18827-5. 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.