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

Angela Andreella (), Antonietta Mira (), Spyros Balafas (), Ernst-Jan C. Wit (), Fabrizio Ruggeri (), Giovanni Nattino (), Giulia Ghilardi () and Guido Bertolini ()
Additional contact information
Angela Andreella: Università degli studi dell’Insubria
Antonietta Mira: Università degli studi dell’Insubria
Spyros Balafas: Università degli studi dell’Insubria
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

Statistical Methods & Applications, 2024, vol. 33, issue 2, No 10, 635-659

Abstract: 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.

Keywords: Decision support system; Emergency call data; Emergency departments data; Generalized nonlinear auto-regressive additive model; Predictive models (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10260-023-00725-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stmapp:v:33:y:2024:i:2:d:10.1007_s10260-023-00725-x

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

DOI: 10.1007/s10260-023-00725-x

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().