AI 中文总结
该研究通过将美国各州感染数据建模为点过程，分离振幅与相位变异，结合Wasserstein PCA和线性向量回归，发现更早实施管控措施可平缓COVID-19感染曲线。
AI 中文摘要
本研究探究疫情早期政府管控措施与COVID-19传播及时间动态的关联。我们将美国各州每日感染数据建模为点过程的实现，以随机强度测度作为潜在研究对象，关键在于允许这些实现不仅在幅度上变化，还在时间动态上变化。通过非参数分离这些振幅和相位变异，我们分析政府管控措施与每种变异来源的关联，将感染曲线与同期的牛津严格指数（Oxford Stringency Index）关联。采用Wasserstein主成分分析（Wasserstein PCA），我们分析感染和管控措施的时间变异性。随后，我们将所得得分、代表总体严格性预算的标量以及总感染数作为输入，用于线性向量对向量回归模型。研究结果表明，当考虑振幅和相位变异的单独贡献时，更早实施管控措施与更平缓的感染曲线相关；相比之下，我们未发现严格性与总感染数之间，或总体严格性预算与所考虑的感染结果之间存在关联的显著证据。
英文摘要
We investigate how governmental restrictions relate to the spread and temporal dynamics of COVID-19 early in the pandemic. We model daily infection data from each US state as realisations of a point process, taking the random intensity measure to be the latent object of interest and, crucially, allowing these realisations to vary not only in magnitude but also in the temporal dynamics. By non-parametrically separating these amplitude and phase variations, we examine how government restrictions relate to each source of variability, relating the infection curves to the Oxford Stringency Index, which we treat as a measure on the same time window. Employing Wasserstein PCA, we analyse the temporal variability of both the infections and the restrictions. We then use the resulting scores, together with the scalars representing the overall stringency budget and the total infection count, as inputs to a linear vector-on-vector regression model. Our findings suggest that, when considering the separate contributions of amplitude and phase variability, earlier implementation of restrictions is associated with flatter infection curves. By contrast, we do not find significant evidence of an association between stringency and total infection counts, nor between the overall stringency budget and the infection outcomes considered.
