Scholarlink

Purpose <p>This study aims to develop and validate a mathematical model for estimating the number of emergency physicians required in post-earthquake scenarios, using actual data from the 2023 Kahramanmaraş earthquakes in Türkiye.</p> Methodology <p>The methodology follows a structured five-step framework to assess earthquake impact and emergency healthcare demand. First, population impact is analyzed using USGS PAGER data to estimate exposure levels. Second, household and building stock characteristics are profiled from TurkStat, focusing on construction year and building types, which influence structural vulnerability. Third, collapse probabilities are determined through empirical fragility functions that relate earthquake intensity to building failure rates. Fourth, casualties are estimated by combining structural damage with fatality and injury ratios specific to building types. Finally, physician demand is calculated using the M/M/s queuing theory model, incorporating key variables, such as emergency healthcare capacity, patient arrival rates, and examination duration, to forecast medical staffing needs in the aftermath of a disaster.</p> Findings <p>The model estimated 11,645 potential emergency department visits in Hatay province within the first 144&#xa0;h post-earthquake. Based on this, 27 emergency physicians per shift—or 81 total physicians across three shifts—are required to operate at full capacity. These figures closely align with actual post-disaster hospital admission data, validating the model.</p> Conclusions <p>This study presents a scalable, actual-data-validated model for physician workforce planning in disaster scenarios. The queuing-based approach supports strategic resource allocation and enhances organizational resilience. Unlike existing models, this study directly integrates field-specific damage and population data to forecast real-time health system needs.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Demand–capacity estimation using queueing theory: application to hospital resource planning in the 2023 Türkiye earthquake

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月26日

查看全文
  • Bircan Kara,
  • Ali Utku Şahin

摘要

Purpose

This study aims to develop and validate a mathematical model for estimating the number of emergency physicians required in post-earthquake scenarios, using actual data from the 2023 Kahramanmaraş earthquakes in Türkiye.

Methodology

The methodology follows a structured five-step framework to assess earthquake impact and emergency healthcare demand. First, population impact is analyzed using USGS PAGER data to estimate exposure levels. Second, household and building stock characteristics are profiled from TurkStat, focusing on construction year and building types, which influence structural vulnerability. Third, collapse probabilities are determined through empirical fragility functions that relate earthquake intensity to building failure rates. Fourth, casualties are estimated by combining structural damage with fatality and injury ratios specific to building types. Finally, physician demand is calculated using the M/M/s queuing theory model, incorporating key variables, such as emergency healthcare capacity, patient arrival rates, and examination duration, to forecast medical staffing needs in the aftermath of a disaster.

Findings

The model estimated 11,645 potential emergency department visits in Hatay province within the first 144 h post-earthquake. Based on this, 27 emergency physicians per shift—or 81 total physicians across three shifts—are required to operate at full capacity. These figures closely align with actual post-disaster hospital admission data, validating the model.

Conclusions

This study presents a scalable, actual-data-validated model for physician workforce planning in disaster scenarios. The queuing-based approach supports strategic resource allocation and enhances organizational resilience. Unlike existing models, this study directly integrates field-specific damage and population data to forecast real-time health system needs.