在稀缺的测试资源下对多维异质人群进行传染病查,并应用于COVID-19
Hussein El Hajj1, Douglas R Bish2, Ebru K Bish2
1Department of Industrial and Systems Engineering Virginia Tech Blacksburg Virginia USA.
Naval research logistics
|April 12, 2024
概括
优化的聚合测试策略显著改善了传染病爆发管理,特别是在COVID-19等大流行期间. 数据驱动型号通过智能分组个人进行测试来提高测试覆盖率并减少伤害.
科学领域:
- 流行病学和公共卫生.
- 运营研究 运营研究
- 传染病管理 传染病管理
背景情况:
- 有效的传染病爆发管理,以COVID-19为例,严重依赖于测试.
- 测试资源有限,需要对谁进行测试做出战略决策,考虑个人风险和潜在危害降低.
- 聚合测试提供了更大的覆盖范围,但通过不完美的测试引入了虚假阴性.
研究的目的:
- 开发数据驱动的优化模型,用于设计聚合测试策略.
- 为应对将异质人群划分为个人测试,聚合测试和非测试组的挑战.
- 优化测试池以最大限度地减少损害或最大限度地提高测试覆盖范围.
主要方法:
- 开发数据驱动的优化模型和算法,用于聚合测试策略设计.
- 不同质的群体被分为三个组:个人测试,聚合测试和没有测试.
- 进一步将组合的测试受试者分为可变大小的测试池.
主要成果:
- 与当前的COVID-19接触者追踪实践相比,拟议的聚合测试策略表现出了相当大的表现.
- 该研究强调了优化测试设计的好处,考虑到人口异质性和有限的能力.
- 数据驱动的模型有效地管理了传染病爆发的复杂测试决策.
结论:
- 优化的聚合测试策略为管理传染病爆发提供了显著的优势.
- 开发的模型为公共卫生测试场景中有效分配资源提供了框架.
- 智能测试设计对于在资源限制下最大限度地提高公共卫生效益至关重要.
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