A structured open dataset of government interventions in response to COVID-19

TLDR

Governments worldwide implemented diverse non‑pharmaceutical interventions during COVID‑19, and documenting these strategies is essential to understand epidemic progression. The study aims to develop a hierarchical coding scheme for NPIs and share the resulting dataset openly to enhance transparency and enable collaborative validation. The authors performed a content analysis of public sources, created a hierarchical NPI coding scheme, provided visualization codes, and plan to update the dataset through December 2020. The resulting structured dataset captures government interventions and their timelines, enabling cross‑country comparison and assessment of NPI impacts on epidemic dynamics, health, economy, and human rights, and serves as a valuable resource for pandemic preparedness.

Abstract

In response to the COVID-19 pandemic, governments have implemented a wide range of non-pharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset until the end of December 2020.