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Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Arthur Pinheiro de Araújo Costa1, Vitor Pinheiro de Araújo Costa2, Daniel Augusto de Moura Pereira3

  • 1Instituto Militar de Engenharia (IME). Praça Gen. Tibúrcio 80, Urca. 22290-270 Rio de Janeiro RJ Brasil. arthurcosta.araujo@ime.eb.br.

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这项研究模拟了巴西.

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科学领域:

  • 医疗保健中的运营研究
  • 卫生系统管理管理卫生系统管理
  • 紧急医疗服务模拟

背景情况:

  • 建模与模拟 (M&S) 提供了一种无风险的方法来分析医疗保健系统,疾病进展和治疗结果.
  • 巴西的移动紧急护理服务 (SAMU) 需要高效的运营策略来管理患者流量和资源分配.
  • 优化救护车服务对于提高统一卫生系统 (SUS) 弹性至关重要.

研究的目的:

  • 在巴西的一个地区使用Arena软件和机器学习 (ML) 模拟SAMU的救护车服务系统.
  • 分析不同资源配置对等待时间和工作量等关键绩效指标的影响.
  • 预测不同数量的救护车对患者等待时间的影响.

主要方法:

  • 量化方法学结合了数学建模和案例研究方法.
  • 利用竞技场软件进行SAMU系统的离散事件模拟.
  • 集成的ML,特别是回归模型,与模拟输出以关联等待时间和救护车号码,参考曼彻斯特议定书.

主要成果:

  • 基于真实数据的模拟结果表明,增加救护车数量可以显著减少患者等待时间.
  • 通过优化救护车部署,观察到精简的资源分配.
  • 综合的M&S和ML方法在各种场景下提供了对系统性能的预测见解.

结论:

  • 该研究表明,优化救护车数量和资源配置可以提高移动紧急服务的运营效率.
  • 提高紧急医疗服务的效率有助于巴西统一卫生系统 (SUS) 的整体弹性和性能.
  • 模拟和机器学习的联合使用为医疗保健系统的规划和改进提供了强大的工具.