Volume 10, Issue 4 (Summer-In Press 2025)
Health in Emergencies and Disasters Quarterly 2025, 10(4): 0-0 |
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Ethics code: https://ethics.research.ac.ir/ IR.GOUMS.REC.1402.127
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Babanezhad M, Khorsha H, Mohajervatan A, Choori A. Estimating the Demand for Ambulances in Traffic Accidents. Health in Emergencies and Disasters Quarterly 2025; 10 (4)
URL: http://hdq.uswr.ac.ir/article-1-641-en.html
URL: http://hdq.uswr.ac.ir/article-1-641-en.html
1- Department of Statistics, Faculty of Sciences, Golestan University, Gorgan, Iran.
2- Department of Management of statistics and information technology ,Golestan University of Medical Sciences, Gorgan, Iran.
3- Department of Anesthesia and Prehospital Emergency care, School of Paramedical Sciences, Golestan University of Medical Sciences, Gorgan, Iran. ,Mohajervatanali@yahoo.com
4- Department of Humanities and Sport Science, Faculty of Sport Sciences, University of Gonbad Kavous, Gonbad, Iran.
2- Department of Management of statistics and information technology ,Golestan University of Medical Sciences, Gorgan, Iran.
3- Department of Anesthesia and Prehospital Emergency care, School of Paramedical Sciences, Golestan University of Medical Sciences, Gorgan, Iran. ,
4- Department of Humanities and Sport Science, Faculty of Sport Sciences, University of Gonbad Kavous, Gonbad, Iran.
Abstract: (69 Views)
Background: Effective Emergency medical service delivery in road traffic accidents requires accurate resource planning that relies on operational, tactical, and strategic demand forecasts. This study aims to estimate the demand for ambulances in traffic accidents using time series modeling techniques.
Methods: We conducted a retrospective cohort analysis of ambulance demands related to traffic incidents in Golestan Province, Iran. The analysis of individual time series was utilized for demand prediction. Then, we applied statistical methods to present the performance indicators.
Results: This research examined 37409 calls that led to ambulance dispatch from March 2021 to March 2023. According to the examination of traffic collision data, the demand rate is greater during the daytime compared to nighttime. Nonetheless, ambulance responses to deadly accidents take place more frequently at night compared to daytime. Our analysis indicates that demand will vary between 2400 and 800 with a 90% confidence level. Additionally, at an 80% confidence level, the demand range is expected to be between 300 and 2800.
Conclusion: By analyzing the historical data, we have identified a trend and seasonal patterns in the data, which suggests an increase in demand during the summer months. Forecasting the course of service recipients in the prehospital emergency service can increase situational awareness and help manage the challenges caused by overcrowding. By anticipating the surge in demand for services during peak periods, it is possible to plan and allocate resources effectively and minimize delays.
Methods: We conducted a retrospective cohort analysis of ambulance demands related to traffic incidents in Golestan Province, Iran. The analysis of individual time series was utilized for demand prediction. Then, we applied statistical methods to present the performance indicators.
Results: This research examined 37409 calls that led to ambulance dispatch from March 2021 to March 2023. According to the examination of traffic collision data, the demand rate is greater during the daytime compared to nighttime. Nonetheless, ambulance responses to deadly accidents take place more frequently at night compared to daytime. Our analysis indicates that demand will vary between 2400 and 800 with a 90% confidence level. Additionally, at an 80% confidence level, the demand range is expected to be between 300 and 2800.
Conclusion: By analyzing the historical data, we have identified a trend and seasonal patterns in the data, which suggests an increase in demand during the summer months. Forecasting the course of service recipients in the prehospital emergency service can increase situational awareness and help manage the challenges caused by overcrowding. By anticipating the surge in demand for services during peak periods, it is possible to plan and allocate resources effectively and minimize delays.
Type of Study: Research |
Subject:
Emergency
Received: 2024/08/10 | Accepted: 2025/01/5 | Published: 2025/07/9
Received: 2024/08/10 | Accepted: 2025/01/5 | Published: 2025/07/9
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This is an open access article distributed under the terms of the Creative Commons Attribution License (CC-By-NC), which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
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