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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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    1. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, December.
    2. Vasiliki Christou & Konstantinos Fokianos, 2014. "Quasi-Likelihood Inference For Negative Binomial Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(1), pages 55-78, January.
    3. Ludwig Fahrmeir & Stefan Lang, 2001. "Bayesian inference for generalized additive mixed models based on Markov random field priors," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(2), pages 201-220.
    4. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
    5. Benjamin Kedem & Konstantinos Fokianos, 2002. "Regression Models for Binary Time Series," International Series in Operations Research & Management Science, in: Moshe Dror & Pierre L’Ecuyer & Ferenc Szidarovszky (ed.), Modeling Uncertainty, chapter 0, pages 185-199, Springer.
    6. Simon N. Wood, 2004. "Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 673-686, January.
    7. X. Lin & D. Zhang, 1999. "Inference in generalized additive mixed modelsby using smoothing splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 381-400, April.
    8. Mohamed A K Al-Azzani & Soheil Davari & Tracey Jane England, 2021. "An empirical investigation of forecasting methods for ambulance calls - a case study," Health Systems, Taylor & Francis Journals, vol. 10(4), pages 268-285, October.
    9. Ricardo D. Kamenetzky & Larry J. Shuman & Harvey Wolfe, 1982. "Estimating Need and Demand for Prehospital Care," Operations Research, INFORMS, vol. 30(6), pages 1148-1167, December.
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