Department of Public Health, 402 E 67th St, New York, NY 10065, USA.
Disaster Med Public Health Prep. 2009 Dec;3 Suppl 2:S121-31. doi: 10.1097/DMP.0b013e3181be9c39.
The public health response to an influenza pandemic or other large-scale health emergency may include mass prophylaxis using multiple points of dispensing (PODs) to deliver countermeasures rapidly to affected populations. Computer models created to date to determine "optimal" staffing levels at PODs typically assume stable patient demand for service. The authors investigated POD function under dynamic and uncertain operational environments.
The authors constructed a Monte Carlo simulation model of mass prophylaxis (the Dynamic POD Simulator, or D-PODS) to assess the consequences of nonstationary patient arrival patterns on POD function under a variety of POD layouts and staffing plans. Compared are the performance of a standard POD layout under steady-state and variable patient arrival rates that may mimic real-life variation in patient demand.
To achieve similar performance, PODs functioning under nonstationary patient arrival rates require higher staffing levels than would be predicted using the assumption of stationary arrival rates. Furthermore, PODs may develop severe bottlenecks unless staffing levels vary over time to meet changing patient arrival patterns. Efficient POD networks therefore require command and control systems capable of dynamically adjusting intra- and inter-POD staff levels to meet demand. In addition, under real-world operating conditions of heightened uncertainty, fewer large PODs will require a smaller total staff than many small PODs to achieve comparable performance.
Modeling environments that capture the effects of fundamental uncertainties in public health disasters are essential for the realistic evaluation of response mechanisms and policies. D-PODS quantifies POD operational efficiency under more realistic conditions than have been modeled previously. The authors' experiments demonstrate that effective POD staffing plans must be responsive to variation and uncertainty in POD arrival patterns. These experiments highlight the need for command and control systems to be created to manage emergency response successfully.
应对流感大流行或其他大规模卫生紧急情况的公共卫生措施可能包括使用多点分发（POD）进行大规模预防，以便迅速向受影响人群提供对策。迄今为止，为确定 POD 人员配备的“最佳”水平而创建的计算机模型通常假定对服务的稳定患者需求。作者研究了在动态和不确定的运营环境下 POD 的功能。
作者构建了大规模预防的蒙特卡罗模拟模型（动态 POD 模拟器，或 D-PODS），以评估在各种 POD 布局和人员配备计划下，非平稳患者到达模式对 POD 功能的后果。比较标准 POD 布局在稳定和变化的患者到达率下的性能，这些到达率可能模拟患者需求的实际变化。
为了达到类似的性能，在非平稳患者到达率下运行的 POD 需要比假设稳定到达率更高的人员配备水平。此外，除非人员配备水平随时间变化以适应不断变化的患者到达模式，否则 POD 可能会出现严重的瓶颈。因此，高效的 POD 网络需要指挥和控制系统，能够动态调整内部和 POD 之间的人员配备水平以满足需求。此外，在公共卫生灾难中存在高度不确定性的实际运行条件下，与许多小 POD 相比，较少的大 POD 需要较小的总人员配备即可实现可比的性能。
对捕捉公共卫生灾难中基本不确定性影响的建模环境进行建模对于对反应机制和政策进行现实评估至关重要。D-PODS 比以前建模的更真实地量化了 POD 的运营效率。作者的实验表明，有效的 POD 人员配备计划必须对 POD 到达模式的变化和不确定性做出响应。这些实验强调了需要创建指挥和控制系统以成功管理应急响应。
