在适应性测试场地分配中平衡访问,准确性和公平性,在乔治亚州亚特兰大对COVID-19的应用程序中
Thomas W Hsiao1, Che-Yi Liao2, Lance A Waller1
1Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, 30322, USA.
Scientific reports
|September 25, 2025
概括
本研究介绍了一种适应性,多目标优化方法,用于分配COVID-19测试站点. 该方法平衡了测试访问,预测准确性和种族平等,优于流行病监测的非适应性策略.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 计算生物学 计算生物学
背景情况:
- 有效的流行病监测依赖于及时的数据和公平的检测机会.
- COVID-19突出了实时疾病监测,热点识别和公平资源分配方面的挑战.
- 传统的公共卫生政策往往忽视了关键因素,如在紧急情况下测试场所的可访问性和种族平等.
研究的目的:
- 开发一种适应性,多目标优化方法,用于在流行病期间分配本地测试站点.
- 提高疾病监测系统的准确性和公平性.
- 加强对传染病爆发和集群的实时响应.
主要方法:
- 贝叶斯空间时间疾病建模与COVID-19病例数据的整合.
- 应用一个多目标优化框架,使用内部点方法解决问题.
- 为动态测试场地分配制定适应性战略.
主要成果:
- 与非自适应性测试场地分配策略相比,自适应性多目标方法显示出更高的性能.
- 提出的方法在所需的时间和资源方面实现了更高的效率.
- 与单个目标优化方法相比,观察到最小的性能牺牲.
结论:
- 开发的方法为优化公共卫生紧急情况的测试场地分配提供了一个强大的框架.
- 这种方法可以提高流行病监测的速度,准确性和公平性.
- 该方法具有改善应对未来本地,季节性和更广泛的流行病爆发的潜力.
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