ObjectivesEuropean countries implemented highly diverse mitigation policies during the COVID-19 pandemic, ranging from strict nationwide lockdowns to more voluntary approaches.This study aims to understanding how timing, stringency, and comprehensiveness of policy responses influenced epidemic trajectories by applying machine learning-based counterfactual analysis. MethodsData from 27 European OECD countries between January 2020 and December 2022 were analysed.Daily epidemiological data, including COVID-19 cases, deaths, effective reproduction number were linked with government response indicators, demographics, vaccination, testing, and mobility data.Temporal Fusion Transformer (TFT) models were applied for multi-horizon time series forecasting by capturing nonlinear relationships between policy response indicators and COVID-19 outcome variables.Simulations were conducted maximum reductions of 7-10% in Belgium, Portugal, and Switzerland under strict scenarios.Over early relaxation from the restrictions might lead to outcomes as adverse as those under continuously loose policies.Feature importance analyses highlighted restrictions on mobility and gatherings, vaccination, testing, and fiscal measures as dominant drivers, alongside country-level factors such as age structure and chronic disease burden. ConclusionsThe TFT-based machine learning framework demonstrated favourable feasibility and interpretability, reinforcing its value in guiding policy decisions.Comprehensive, multidomain interventions outperformed partial or short-lived restrictions.Lifting measures before achieving sufficient immunity and epidemic control posed substantial risks.Stringent policies reduced transmission, but their impact on mortality was constrained, which might be due to demographic and systemic vulnerabilities.Adaptive, data-driven strategies integrating epidemiology, policy, and structural context are essential to strengthen policy responses against future pandemics.
Tang et al. (Sun,) studied this question.