Estimating the causal impact of non-pharmaceutical interventions on COVID-19 spread in seven EU countries via machine learning.

ABSTRACT: During the COVID-19 pandemic, Non-Pharmaceutical Interventions (NPIs) were imposed all over Europe with the intent to reduce infection spread. However, reports on the effectiveness of those measures across different European countries are inconclusive up to now. Moreover, attempts to predict the effect of NPIs in a prospective and dynamical manner with the aim to support decision makers in future global health emergencies are largely lacking. Here, we explore causal machine learning to isolate causal effects of NPIs in observational public health data from seven EU countries, taking into account specific challenges like their sequential nature, effect heterogeneity, time-dependent confounding and lack of robustness due to violated assumptions. In a pseudo-prospective scenario planning anal

SUBMITTER: Guski J 

PROVIDER: S-EPMC11914055 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature