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首页> 外文期刊>Annals of Operations Research >Decision support system for mass dispensing of medications for infectious disease outbreaks and bioterrorist attacks
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Decision support system for mass dispensing of medications for infectious disease outbreaks and bioterrorist attacks

机译：决策支持系统，用于大规模分配用于传染病暴发和生物恐怖袭击的药物

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摘要

A simulation and decision support system, RealOpt, for planning large-scale emergency dispensing clinics to respond to biological threats and infectious disease outbreaks is described. The system allows public health administrators to investigate clinic design and staffing scenarios quickly. RealOpt incorporates efficient optimization technology seamlessly interfaced with a simulation module. The system's correctness and computational advantage are validated via comparisons against simulation runs of the same model developed on a commercial system. Simulation studies to explore facility layout and staffing scenarios for smallpox vaccination and for an actual anthrax-treatment dispensing exercise and post event analysis are presented. The system produces results consistent with the model built on the commercial system, but requires only a fraction of the computational time. Each smallpox scenario runs within 1 CPU minute on RealOpt, versus run times of over 5-10 hours on the commercial system. The system's fast computational time enables its use in large-scale studies, in particular an anthrax response planning exercise involving a county with 864,000 households. The computational effort required for this exercise was roughly 30 min for all scenarios considered, demonstrating that RealOpt offers a very promising avenue for pursuing a comprehensive investigation involving a more diverse set of scenarios, and justifying work towards development of a robust system that can be widely deployed for use by state, local, and tribal health practitioners. Using our staff allocation and assignments for the Anthrax field exercise, DeKalb county achieved the highest throughput among all counties that simultaneously conducted the same scale of Anthrax exercise at various locations, with labor usage at or below the other counties. Indeed, DeKalb exceeded the targeted number of households, and it processed 50% more individuals compared to the second place county. None of the other counties achieved the targeted number of households. The external evaluators commented that DeKalb produced the most efficient floor plan (with no path crossing), the most cost-effective dispensing (lowest labor/throughput value), and the smoothest operations (shortest average wait time, average queue length, equalized utilization rate). The study proves that even without historical data, using our system one can plan ahead and be able to wisely estimate the required labor resources. The exercise also revealed many areas that need attention during the operations planning and design of dispensing centers. The type of disaster being confronted (e.g., biological attack, infectious disease outbreak, or a natural disaster) also dictates different design considerations with respect to the dispensing clinic, facility locations, dispensing and backup strategies, and level of security protection. Depending on the situation, backup plans will be different, and the level of security and military personnel, as well as the number of healthcare workers required, will vary. In summary, the study shows that a real-time decision support system is viable through careful design of a stand-alone simulator coupled with powerful tailor-designed optimization solvers. The flexibility of performing empirical tests quickly means the system is amenable for use in training and preparation, and for strategic planning before and during an emergency situation.
机译：描述了一种模拟和决策支持系统RealOpt，用于规划大规模的紧急配药诊所，以应对生物威胁和传染病的爆发。该系统使公共卫生管理员可以快速调查诊所的设计和人员配置情况。 RealOpt集成了高效的优化技术，该技术与仿真模块无缝连接。通过与商业系统上开发的同一模型的仿真运行进行比较，验证了系统的正确性和计算优势。提出了模拟研究，以探索天花疫苗接种以及实际的炭疽治疗配药演习和事后分析的设施布局和人员配备情况。该系统产生的结果与建立在商业系统上的模型一致，但只需要计算时间的一小部分。在RealOpt上，每个天花场景都在1个CPU分钟内运行，而在商业系统上，则需要5-10个小时以上的运行时间。该系统的快速计算时间使其可用于大规模研究，尤其是涉及一个有864,000户家庭的县的炭疽反应规划活动。对于所有考虑的场景，此练习所需的计算工作大约为30分钟，这表明RealOpt提供了非常有希望的途径，可以进行涉及更多种场景的全面调查，并为开发可广泛使用的强大系统辩护部署供州，地方和部落卫生从业人员使用。使用我们的人员分配和炭疽实地演习分配，迪卡尔布县在所有县中实现了最高的通量，同时在不同地点同时进行了相同规模的炭疽演习，而其他县或县以下的劳动力使用情况则是如此。实际上，迪卡尔布（DeKalb）超出了目标家庭数量，与第二名县相比，它处理的个人数量增加了50％。其他县均未达到目标家庭数。外部评估人员评论说，DeKalb制定了最有效的平面图（无路径交叉），最具成本效益的分配（最低的人工/生产量值）和最顺畅的操作（最短的平均等待时间，平均队列长度，均等的利用率） ）。研究证明，即使没有历史数据，使用我们的系统也可以提前计划并能够明智地估计所需的劳动力资源。演习还揭示了配送中心的运营规划和设计过程中需要注意的许多领域。面临的灾难类型（例如，生物攻击，传染病暴发或自然灾害）还针对配药诊所，设施位置，配药和备用策略以及安全保护级别，规定了不同的设计考虑因素。根据情况，备份计划将有所不同，安全和军事人员的级别以及所需的医护人员的数量也将有所不同。总而言之，研究表明，通过精心设计独立模拟器以及强大的量身定制的优化求解器，实时决策支持系统是可行的。快速执行经验测试的灵活性意味着该系统适合在紧急情况发生之前和期间用于培训和准备以及战略计划。

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