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基于离散事件仿真的模型,用于优化设计和确定移动 COVID-19 唾液检测站的规模。

A Discrete Event Simulation-Based Model to Optimally Design and Dimension Mobile COVID-19 Saliva-Based Testing Stations.

机构信息

From the Enterprise Systems Optimization Lab, Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Champaign, IL.

出版信息

Simul Healthc. 2021 Apr 1;16(2):151-152. doi: 10.1097/SIH.0000000000000565.

Abstract

The present COVID-19 brief report addresses: (1) the problem of optimal design and resource allocation to mobile testing stations to ensure rapid results to the persons getting tested; (2) the proposed solution through a newly developed discrete event simulation model, experienced in on-campus saliva-based testing stations at the University of Illinois at Urbana-Champaign; and (3) the lessons learned on how 10,000 samples (from noninvasive polymerase chain reaction COVID-19 tests) can be processed per day on campus, as well as how the model could be reused or adapted to other contexts by site managers and decision makers.

摘要

本 COVID-19 简要报告涉及:(1) 优化设计和资源分配给移动检测站的问题,以确保接受检测的人员快速获得结果;(2) 通过在伊利诺伊大学厄巴纳-香槟分校校园内唾液检测站新开发的离散事件模拟模型提出的解决方案;(3) 了解如何在校园内每天处理 10000 个样本(来自非侵入性聚合酶链反应 COVID-19 测试),以及站点管理人员和决策者如何重复使用或改编该模型以适应其他环境的经验教训。

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