将测试量集成到用于传染病监测的强盗算法中
Joshua L Warren1,2, Ottavia Prunas3, A David Paltiel2,4
1Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
概括
新的移动测试方法通过考虑可变的测试量来改善传染病监测. 这些方法最大限度地识别受感染的个体,对于疾病控制至关重要.
科学领域:
- 流行病学和公共卫生.
- 生物统计学 生物统计学
- 传染病监测 传染病监测 传染病
背景情况:
- 移动测试服务对于积极监测传染病至关重要,特别是在难以接触的人群中.
- 识别感染者是减轻疾病传播的关键.
- 已使用多臂强盗采样方法来最大限度地提高阳性测试产量.
研究的目的:
- 调查不同地点的可变测试量对多武装强盗采样有效性的影响.
- 开发和评估扩展的采样框架,以考虑测试量变化.
主要方法:
- 扩大了现有的多臂强盗采样框架,以纳入测试体积的变化.
- 在刚果-布拉扎维尔进行了使用人类免疫缺陷病毒 (HIV) 特征的模拟研究.
- 将新方法应用于康涅狄格州的COVID-19测试数据.
主要成果:
- 与现有方法相比,新开发的方法在短期和长期表现上有所改善.
- 扩展框架有效地管理了不同地点的测试量变化.
- 这些方法保持了计算可处理性.
结论:
- 开发的方法在测试量不同时,为积极的传染病监测提供了增强的性能.
- 这些计算可处理的方法被推用于公共卫生环境中的实际实施.
- 为优化疾病监测策略,对测试量变量的计算至关重要.
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