用于季节性传染病干预措施因果评估的贝叶斯流行病对齐方法
Bayesian epidemic alignment for causal evaluation of seasonal infectious-disease interventions
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中文总结 AI 辅助
该研究提出贝叶斯因果计数模型，通过季节特异性仿射变换对齐流行病，以更准确评估季节性传染病干预措施的因果效应，并结合模拟与加泰罗尼亚实际数据验证框架有效性。
中文摘要 AI 辅助
季节性传染病干预措施通常采用中断时间序列或前后设计进行评估，这类设计通过日历周对齐流行病。当不同季节的流行病起始时间、传播速度或峰值时间存在差异时，此类比较会混淆流行病阶段的变化与疾病负担的改变。我们提出一种贝叶斯因果计数模型，其中季节特异性仿射变换将日历时间映射为潜在流行病时钟，干预效应在该时钟而非日历上进行估计。这种对齐是模型的组成部分而非预处理步骤，因此流行病时间的不确定性会传递到每个因果对比中。该模型采用负二项分布作为观测分布，包含分层区域、季节及区域-季节效应，收缩傅里叶流行病曲线，以及连续项目强度暴露。后验g-计算可得出避免病例数、避免比例、峰值衰减和流行病位移，包括受控对比与在每组内传播疾病史的动态对比。一项两级模拟研究在时间稳定、流行病时钟变化、强度依赖的 ascertainment（确诊）及区域层面混杂的情况下评估了偏差、均方根误差、区间覆盖率和参数恢复。我们使用加泰罗尼亚初级保健监测和呼吸道合胞病毒免疫接种的公开数据阐释该框架，明确关注用于识别效应的项目强度重叠情况。
英文摘要
Seasonal infectious-disease interventions are commonly evaluated with interrupted time-series or pre--post designs that align epidemics by calendar week. When epidemic onset, speed or peak timing differs between seasons, such comparisons confound a shift in epidemic phase with a change in disease burden. We propose a Bayesian causal count model in which season-specific affine transformations map calendar time to a latent epidemic clock, and intervention effects are estimated on that clock rather than on the calendar. The alignment is a model component rather than a preprocessing step, so uncertainty about epidemic timing propagates into every causal contrast. The model uses a negative-binomial observation distribution, hierarchical area, season and area-season effects, a shrunk Fourier epidemic curve, and a continuous programme-intensity exposure. Posterior g-computation yields prevented cases, prevented fractions, peak attenuation and epidemic displacement, under both a controlled contrast and a dynamic contrast that propagates disease history within each arm. A two-tier simulation study evaluates bias, root mean squared error, interval coverage and parameter recovery under stable timing, epidemic-clock variation, intensity-dependent ascertainment and area-level confounding. We illustrate the framework using open Catalan primary-care surveillance and respiratory syncytial virus immunisation data, with explicit attention to the overlap in programme intensity that identifies the effect.
