Emergency Medical Service (EMS) systems aim at providing immediate medical care in case of emergency. A careful planning is a major prerequisite for the success of an EMS system, in particular to reduce the response time to emergency calls. Unfortunately, the demand for emergency services is highly variable and uncertainty should not be neglected while planning the activities. Thus, it is of fundamental importance to predict the number of future emergency calls and their interarrival times to support the decision-making process. In this paper, we propose a Bayesian model to predict the number of emergency calls in future time periods. Calls are described by means of a generalized linear mixed model, whose posterior densities of parameters are obtained through Markov Chain Monte Carlo simulation. Moreover, predictions are given in terms of their posterior predictive probabilities. Results from the application to a relevant real case show the applicability of the model in the practice and validate the approach

(2017). A Bayesian model for describing and predicting the stochastic demand of emergency calls . Retrieved from http://hdl.handle.net/10446/171362

A Bayesian model for describing and predicting the stochastic demand of emergency calls

Lanzarone, Ettore;
2017

Abstract

Emergency Medical Service (EMS) systems aim at providing immediate medical care in case of emergency. A careful planning is a major prerequisite for the success of an EMS system, in particular to reduce the response time to emergency calls. Unfortunately, the demand for emergency services is highly variable and uncertainty should not be neglected while planning the activities. Thus, it is of fundamental importance to predict the number of future emergency calls and their interarrival times to support the decision-making process. In this paper, we propose a Bayesian model to predict the number of emergency calls in future time periods. Calls are described by means of a generalized linear mixed model, whose posterior densities of parameters are obtained through Markov Chain Monte Carlo simulation. Moreover, predictions are given in terms of their posterior predictive probabilities. Results from the application to a relevant real case show the applicability of the model in the practice and validate the approach
Nicoletta, Vittorio; Lanzarone, Ettore; Guglielmi, Alessandra; Bélanger, Valérie; Ruiz, Angel
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/171362
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