Predicción de casos de COVID-19 y modelo de localización asignación de bases y ambulancias considerando factores de vulnerabilidad

Translated title of the contribution: Prediction of COVID-19 cases and location-allocation optimization model for bases and ambulances considering vulnerability factors

Samantha Reid Calderón, Orietta Nicolis, Billy Peralta, Franco Menares

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This work addresses the problem of strategic location of bases and ambulances, considering the number of inhabitants and a vulnerability weight, confirmed by socioeconomic and epidemiological elements. To this aim, we use a generalized linear model (GLM) for predicting the COVID-19 cases and a mathematical optimization model for location and allocation which maximizes coverage of population care. The methodology is applied in the Metropolitan region in Chile, analyzing the current situation of the institution of the Emergency Medical Attention Service (SAMU), institution in charge of ambulance management in the region. Likewise, the Social Priority Index (IPS) will be used as a socioeconomic factor and the number of patients confirmed by COVID-19 from March 30 to June 12, 2020. In the results, for the prediction model, a consistent projection was obtained for one week of study, with acceptable residual errors. For the optimization model, the action of the vulnerability is verified, both for a reassignment of ambulances in the system and for the incorporation of bases and/or ambulances, obtaining results in acceptable calculation times.

Translated title of the contributionPrediction of COVID-19 cases and location-allocation optimization model for bases and ambulances considering vulnerability factors
Original languageSpanish
Pages (from-to)564-582
Number of pages19
JournalIngeniare
Volume29
Issue number3
DOIs
Publication statusPublished - 1 Sept 2021

ASJC Scopus subject areas

  • Engineering(all)

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