ArticleHuman resources for health2025
Demand-capacity estimation using queueing theory: application to hospital resource planning in the 2023 Türkiye earthquake.
Article in Human resources for health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThis 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.
findingsThe 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.
conclusionsThis 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.
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