优化检测协议以选COVID-19:一个多目标模型
Hadi Moheb-Alizadeh1,2, Donald P Warsing3, Richard E Kouri4
1Graduate Program in Operations Research, North Carolina State University, Raleigh, NC, 27695, USA.
Health care management science
|October 11, 2024
概括
这项研究引入了一种多目标模拟化 (MOSA) 算法,以优化传染病检测协议,平衡成本,感染传播和错误阴性,用于学校的COVID-19控制.
科学领域:
- 流行病学和公共卫生.
- 计算建模 计算建模
- 运营研究 运营研究
背景情况:
- 随着COVID-19的爆发,人们更需要有效的传染病查策略.
- 优化测试协议涉及平衡多个相互竞争的目标,如成本和疾病传播.
- 现有的方法可能无法充分解决现实世界查场景在集体环境中的复杂性.
研究的目的:
- 为优化传染病检测协议开发和介绍一种新的多目标模拟化 (MOSA) 算法.
- 将这个算法应用于K-12学区内COVID-19查的背景.
- 提供一个可扩展和可适应的工具,用于在各种集体环境中设计有效的测试策略.
主要方法:
- 开发一个多目标模拟回火 (MOSA) 算法.
- 作为计算引擎,整合了一种易感暴露感染康复 (SEIR) 流行病学模型.
- 优化重点是尽量减少测试材料成本,总感染和虚假阴性在定义的测试时间范围内.
主要成果:
- MOSA算法成功生成了针对传染病的最佳测试协议.
- 在推北卡罗来纳州K-12学区的查策略中证明了应用.
- 该方法具有可扩展性,可以适应学校,企业和养老院等多样化的群众环境.
结论:
- 开发的MOSA算法为优化传染病检测协议提供了一个强大的框架.
- 调查结果为有关COVID-19和未来流行病控制的政策决策提供了宝贵的见解.
- 该工具可以在多个测试站点生成特定位置或共同的协议.
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