IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0242305.html
   My bibliography  Save this article

Analysis of the SARS-CoV-2 epidemic in Italy: The role of local and interventional factors in the control of the epidemic

Author

Listed:
  • Daniele Lilleri
  • Federica Zavaglio
  • Elisa Gabanti
  • Giuseppe Gerna
  • Eloisa Arbustini

Abstract

Containment measures have been applied in several countries in order to limit the diffusion of the SARS-CoV-2 epidemic. The scope of this study is to analyze the evolution of the first wave of the SARS-CoV-2 epidemic throughout Italy and factors associated to the different way it spread in the Italian Regions, starting from the day that the first indigenous cases were detected through day 81 (6 days after the end of the strict lockdown). Data were obtained from daily reports and are represented as number (and percentage) of cases/100,000 persons. A lockdown with movement restrictions, especially across Regions, was declared at day 20. At day 81, 219,070 cases (363/100,000 persons) were diagnosed. A regression analysis based on the Gompertz model predicts a total number 233,606 cases (386/100,000 persons) at the end of the epidemic. The 21 areas, divided into Italian Regions and autonomous Provinces, showed a wide range in the frequency of cases at day 81 (58–921, median 258/100,000 persons) and total predicted cases (58–946, median 267/100,000 persons). Similarly, the predicted time for the end of the wave of the epidemic (considering as surrogate marker the time at which 99% of the total cases are predicted to occur) was highly variable, ranging from 64 to 136 (median 99) days. We analyzed the impact of local and interventional variables on the epidemic curve in each Region. The number of cases correlated inversely with the distance from the area in which first cases were detected and directly also with the gross domestic product pro capite (as a marker of industrial activity) of the Region. Moreover, an earlier start of the lockdown (i.e. in the presence of a lower number of cases) and wider testing were associated with a lower final number of total cases. In conclusion, this analysis shows that population-wide testing and early lockdown enforcement appear effective in limiting the spreading of the SARS-CoV-2 epidemic.

Suggested Citation

  • Daniele Lilleri & Federica Zavaglio & Elisa Gabanti & Giuseppe Gerna & Eloisa Arbustini, 2020. "Analysis of the SARS-CoV-2 epidemic in Italy: The role of local and interventional factors in the control of the epidemic," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-12, November.
    • Handle: RePEc:plo:pone00:0242305
    DOI: 10.1371/journal.pone.0242305
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242305
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0242305&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0242305?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. Se Yoon Lee & Bowen Lei & Bani Mallick, 2020. "Estimation of COVID-19 spread curves integrating global data and borrowing information," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-17, July.
    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. Pelinovsky, E. & Kokoulina, M. & Epifanova, A. & Kurkin, A. & Kurkina, O. & Tang, M. & Macau, E. & Kirillin, M., 2022. "Gompertz model in COVID-19 spreading simulation," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    2. Conceição Leal & Leonel Morgado & Teresa A. Oliveira, 2023. "Mathematical and Statistical Modelling for Assessing COVID-19 Superspreader Contagion: Analysis of Geographical Heterogeneous Impacts from Public Events," Mathematics, MDPI, vol. 11(5), pages 1-18, February.
    3. Christian Alemán & Christopher Busch & Alexander Ludwig & Raül Santaeulà lia-Llopis, 2020. "Evaluating the Effectiveness of Policies Against a Pandemic," Working Papers 2020-078, Human Capital and Economic Opportunity Working Group.
    4. Se Yoon Lee & Bani K. Mallick, 2022. "Bayesian Hierarchical Modeling: Application Towards Production Results in the Eagle Ford Shale of South Texas," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 1-43, May.
    5. Stef Baas & Sander Dijkstra & Aleida Braaksma & Plom Rooij & Fieke J. Snijders & Lars Tiemessen & Richard J. Boucherie, 2021. "Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units," Health Care Management Science, Springer, vol. 24(2), pages 402-419, June.
    6. Demetrius E. Davos & Ioannis C. Demetriou, 2022. "Convex-Concave fitting to successively updated data and its application to covid-19 analysis," Journal of Combinatorial Optimization, Springer, vol. 44(5), pages 3233-3262, December.
    7. Lin, Jilei & Eck, Daniel J., 2021. "Minimizing post-shock forecasting error through aggregation of outside information," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1710-1727.
    8. Dijkstra, Sander & Baas, Stef & Braaksma, Aleida & Boucherie, Richard J., 2023. "Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy," Omega, Elsevier, vol. 116(C).
    9. Ángel Berihuete & Marta Sánchez-Sánchez & Alfonso Suárez-Llorens, 2021. "A Bayesian Model of COVID-19 Cases Based on the Gompertz Curve," Mathematics, MDPI, vol. 9(3), pages 1-16, January.
    10. Se Yoon Lee, 2022. "Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications," Mathematics, MDPI, vol. 10(6), pages 1-51, March.
    11. Jesus Cerquides, 2021. "A First Approach to Closeness Distributions," Mathematics, MDPI, vol. 9(23), pages 1-12, December.

    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:plo:pone00:0242305. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.