IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v33y2024i2d10.1007_s10260-023-00725-x.html
   My bibliography  Save this article

A predictive model for planning emergency events rescue during COVID-19 in Lombardy, Italy

Author

Listed:
  • Angela Andreella

    (Università degli studi dell’Insubria
    Università Ca’ Foscari di Venezia)

  • Antonietta Mira

    (Università degli studi dell’Insubria
    Università della Svizzera Italiana)

  • Spyros Balafas

    (Università degli studi dell’Insubria
    Università Vita-Salute San Raffaele)

  • Ernst-Jan C. Wit

    (Università della Svizzera Italiana)

  • Fabrizio Ruggeri

    (Consiglio Nazionale delle Ricerche, Istituto di Matematica Applicata e Tecnologie Informatiche)

  • Giovanni Nattino

    (Istituto di Ricerche Farmacologiche Mario Negri, IRCCS)

  • Giulia Ghilardi

    (Istituto di Ricerche Farmacologiche Mario Negri, IRCCS)

  • Guido Bertolini

    (Istituto di Ricerche Farmacologiche Mario Negri, IRCCS)

Abstract

Forecasting the volume of emergency events is important for resource utilization in emergency medical services (EMS). This became more evident during the COVID-19 outbreak when emergency event forecasts used by various EMS at that time tended to be inaccurate due to fluctuations in the number, type, and geographical distribution of these events. The motivation for this study was to develop a statistical model capable of predicting the volume of emergency events for Lombardy’s regional EMS called AREU at different time horizons. To accomplish this goal, we propose a negative binomial additive autoregressive model with smoothing splines, which can predict over-dispersed counts of emergency events one, two, five, and seven days ahead. In the model development stage, a large set of covariates was considered, and the final model was selected using a cross-validation procedure that takes into account the observations’ temporal dependence. Comparisons of the forecasting performance using the mean absolute percentage error showed that the proposed model outperformed the model used by AREU, as well as other widely used forecasting models. Consequently, AREU decided to adopt the new model for its forecasting purposes.

Suggested Citation

  • Angela Andreella & Antonietta Mira & Spyros Balafas & Ernst-Jan C. Wit & Fabrizio Ruggeri & Giovanni Nattino & Giulia Ghilardi & Guido Bertolini, 2024. "A predictive model for planning emergency events rescue during COVID-19 in Lombardy, Italy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(2), pages 635-659, April.
    • Handle: RePEc:spr:stmapp:v:33:y:2024:i:2:d:10.1007_s10260-023-00725-x
    DOI: 10.1007/s10260-023-00725-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-023-00725-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10260-023-00725-x?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.

    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:spr:stmapp:v:33:y:2024:i:2:d:10.1007_s10260-023-00725-x. 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.springer.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.