Governments are attempting to control the COVID-19 pandemic with nonpharmaceutical interventions (NPIs). However, it is still largely unknown how effective different NPIs are at reducing transmission.

Data-driven studies can estimate the effectiveness of NPIs while minimising assumptions, but existing analyses lack sufficient data and validation to robustly distinguish the effects of individual NPIs.

We gather chronological data on NPIs in 41 countries between January and the end of May 2020, creating the largest public NPI dataset collected with independent double entry. We then estimate the effectiveness of 8 NPIs with a Bayesian hierarchical model by linking NPI implementation dates to national case and death counts.

The results are supported by extensive empirical validation, including 11 sensitivity analyses with over 200 experimental conditions.

We find that closing schools and universities was highly effective; that banning gatherings and closing high-risk businesses was effective, but closing most other businesses had limited further benefit; and that many countries may have been able to reduce R below 1 without issuing a stay-at-home order.

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