从常规监测数据中推断多种病原体,变种,亚型或血清型的时间趋势
Oliver Eales1,2, Saras M Windecker3, James M McCaw1,2
1Infectious Disease Dynamics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia.
American journal of epidemiology
|June 7, 2025
概括
这项研究引入了一个统计框架,以准确跟踪个人传染病趋势,如流感和SARS-CoV-2,从联合监测数据. 这有助于更好地了解疾病的传播和干预的有效性.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 传染病监测通常使用组合指标 (例如,类似流感的疾病),结合多种病原体.
- 复合指标可能会掩盖个别病原体的动态,并受到测试变化的影响.
- 准确的时间趋势估计对于疾病监测和干预评估至关重要.
研究的目的:
- 开发一个一般的统计框架,从复合监测数据中推断个别病原体的时间趋势.
- 提供一种可靠的方法来解开混合监视信号中的病原体特定动态.
主要方法:
- 开发了一种用于多病原体趋势推断的新型统计框架.
- 将框架应用于多个国家的多种监测系统 (流感,SARS-CoV-2,登革热).
- 在各种流行病情景和报告决议 (每周,每天) 中验证了方法.
主要成果:
- 从复合数据中成功推断出单个病原体的独特时间趋势.
- 在不同的病原体,位置和流行病模式中表现出强度.
- 展示了对不同复杂度的真实世界监控数据的适用性.
结论:
- 开发的框架准确地从例行监测数据中估计了个别病原体的趋势.
- 这种方法提高了监测特定传染病和评估公共卫生干预措施的能力.
- 适用于全球范围广泛的病原体和监测系统.
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