The rapid rollout of COVID-19 vaccines in the United Kingdom in early 2021 differed markedly from that of many other European countries, providing a natural setting to assess the impact of vaccination speed on public health outcomes. We evaluate the impact of the accelerated UK vaccination rollout and associated policy transition on COVID-19 mortality and transmission dynamics by constructing a probabilistic reference trajectory for the UK under a slower vaccination and reopening trajectory. The proposed framework combines ideas from interrupted time series analysis and synthetic control methods with flexible probabilistic modelling based on multi-output Gaussian processes. These models capture non-linear and heterogeneous dependence structures across countries and over time, while providing uncertainty quantification through predictive distributions. A central feature of the methodology is a design-consistent validation strategy based on predictive performance in held-out pre-intervention periods, which is used both to guide model specification and to assess the plausibility of the reconstructed reference trajectory. The empirical results indicate a substantial reduction in COVID-19 mortality associated with the accelerated vaccination-policy transition, with little evidence of an effect on transmission rates. Generally, the framework illustrates how flexible probabilistic models and predictive validation can support causal and policy evaluation in complex time series settings.

翻译：2021年初英国快速推进COVID-19疫苗接种的进程与欧洲多国形成鲜明对比，这为评估疫苗接种速度对公共卫生结果的影响提供了自然实验场景。我们通过构建英国在较慢疫苗接种和开放轨迹下的概率参考轨迹，评估加速疫苗接种计划及相关政策转变对COVID-19死亡率和传播动态的影响。该框架融合了中断时间序列分析与合成控制方法的思想，并基于多输出高斯过程构建灵活的概率模型。这些模型能捕捉国家间及时间维度上的非线性异质依赖结构，同时通过预测分布提供不确定性量化。该方法的核心特征是基于干预前保留时段预测性能的设计一致性验证策略，该策略既用于指导模型规范，也用于评估重构参考轨迹的合理性。实证结果表明，加速疫苗接种政策转变与COVID-19死亡率显著降低相关，但未发现对传播速率产生影响的明显证据。总体而言，该框架展示了灵活概率模型与预测验证如何支持复杂时间序列环境中的因果推断与政策评估。