复杂人群中流行病的时间序列建模:检测随时间变化发生率波动的变化
Rachael Aber1,2,3, Yanming Di2, Benjamin D Dalziel1,4
1Department of Integrative Biology, Oregon State University, Corvallis, Oregon, United States of America.
PLoS computational biology
|July 11, 2025
概括
这项研究引入了一种新的统计方法,用于测量传染病发病率波动性随时间变化的变化. 这些发现揭示了COVID-19分散的变化,不仅仅是由病例数量解释的,表明传播动态的潜在变化.
科学领域:
- 流行病学 流行病学
- 统计建模 统计建模
- 公共卫生 公共卫生
背景情况:
- 传染病发病率趋势对于了解流行病动态和控制策略至关重要.
- 发病率数据的时间波动,经常被忽视,可以揭示潜在的流行病驱动因素,如传播异质性和报告波动.
- 目前的分析通常依赖于移动平均值,可能错过了流行病行为中的关键转变.
研究的目的:
- 开发一个统计框架来量化发生率分散的时间变化.
- 检测分散参数的快速变化,这可能标志着新的流行病阶段.
- 将这一框架应用于美国各县的COVID-19发病率数据,以确定流行动态的变化.
主要方法:
- 开发了一个统计框架来量化发生率分散的时间变化.
- 该方法应用于来自美国144个县的COVID-19发病率数据 (2020年1月 - 2023年3月).
- 分析分散的时间趋势,将其与发病率数据进行比较,并确定转移.
主要成果:
- 揭示了分散的明显时间趋势,仅仅由发病率无法完全解释.
- 在2022年主要的COVID-19病例激增周围观察到扩散的增加.
- 发现高度过度分散的模式在时间序列中以后变得更频繁,在各县复制.
结论:
- 分散转移可以表明传输异质性,易感性或报告的变化.
- 开发的方法为公共卫生官员提供了一个工具,以预测和管理流行病制度的转变.
- 量化时间分散为流行病驱动因素提供了更深入的见解,超出了简单的发病率趋势.
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