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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
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    References listed on IDEAS

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