A database for determining the location and quantity of ambulances that must be available in a emergency medical care

This dataset was developed to support research and decision-making regarding the ideal location and quantity of ambulances in emergency medical services. It is primarily intended for discrete event simulation (DES) applications and was generated with support from the NotebookLM tool, used to define the emergency care flowchart and the probability distributions for response times. The dataset was constructed using simulated data representing the city of Sorocaba (SP, Brazil), and can be easily adapted to other municipalities using the Google Colab environment. Care Flowchart and Time Markers (T1 to T4) · T1 – Call arrival: The emergency call is received by the system. · T2 – Dispatch and travel to the incident: A team is assigned, prepares, and departs to the scene. · T3 – On-site care: If necessary, initial care is provided at the incident location. · T4 – Transport and vehicle release: The patient is transported to the hospital. The team transfers the patient, releases the stretcher, sanitizes the vehicle, and returns to base, becoming available for new calls. Probability Distributions Used - On-site care time (T3): Triangular distribution with a minimum of 6 minutes, mode of 11.9 minutes, and maximum of 38 minutes. - Type of care (Care_type): 95% of calls are for basic life support (BLS) and 5% for advanced life support (ALS). - Daily demand pattern: · 00:00–08:00: 20% of daily calls · 08:00–16:00: 35% of daily calls · 16:00–00:00: 45% of daily calls CSV File Format Column Type Description Day int64 Simulation day number (starting from 0). Time object Call arrival time (HH:MM format). latitude float64 Simulated latitude of the incident. longitude float64 Simulated longitude of the incident. T3 float64 On-site care time in minutes. T4 float64 Transport and hospital release time in minutes. Care_type object Type of care ("basic" or "advanced"). Dataset Applications · Modeling and simulation of healthcare systems. · Studies on ambulance allocation. · Impact analysis of response times. · Support for public policy and management decisions. Access and Reproducibility · Download .CSV: https://drive.google.com/file/d/1p7L_xVoSPbdlMX2fG4YuehSjkKI9teA0/view?usp=sharing · Regenerate data on Google Colab: https://colab.research.google.com/drive/1lcMefMTOU3rxcWKmTO8blZREKL5_QLUg?usp=sharing Related Publications Gigante, R. L., & Azevedo, A. T. (2022). Study of the impact of the start time of work shift on the efficiency of an emergency system through a simulation model of discrete events. Gestão & Produção, 29, e4421. DOI: https://doi.org/10.1590/1806-9649-2022v29e4421 Gigante, R. L., Azevedo, A. T. de, & Ohishi, T. (2024). Optimizing ambulance base locations: A clustering-based approach. In 5th South American IEOM Conference. DOI: https://doi.org/10.46254/SA05.20240171