Evaluating the Impact of COVID-19 Vaccination in the United Kingdom: A Gaussian Process Approach

Gianluca Giudice; Sara Geneletti; Konstantinos Kalogeropoulos

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arXiv · 2210.02850

Evaluating the Impact of COVID-19 Vaccination in the United Kingdom: A Gaussian Process Approach

Gianluca Giudice·Sara Geneletti·Konstantinos Kalogeropoulos

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Abstract

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.

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Gianluca Giudice, Sara Geneletti, Konstantinos Kalogeropoulos. 2026-05-30. Evaluating the Impact of COVID-19 Vaccination in the United Kingdom: A Gaussian Process Approach. https://arxiv.org/abs/2210.02850

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