During the COVID-19 pandemic, interventions were decided under rapidly changing, conjectural conditions, with limited (if any) prior experience. Worldwide, governments have implemented country-specific control strategies to prevent the introduction and mitigate the spread of the virus. However, were those measures effective?

Researchers at the Complexity Science Hub Vienna are building a comprehensive database of non-pharmaceutical interventions (NPIs) taken by the governments worldwide in order to assess the impact of these actions on the spread of the COVID-19 in the respective countries.


Students, researchers, and volunteers are collecting data from public sources on the implemented NPIs, including the time schedules for the implementation.

The dataset describes the implemented NPIs for 57 countries, including the Diamond Princess cruise ship. Measures implemented at the subnational level (state, region, city) are also included.

The CCCSL provides the date of implementation of the NPI. Date of announcement was used when the date of implementation of the NPI could not be found (this is then specified in the field Comment).

NPIs are listed in a standardized manner, i.e. classified using a four-level hierarchical coding scheme (theme/category/subcategory/code). Eight major themes (level 1, L1 of the classification scheme) were identified and each NPI is assigned to one of them:

    1. Case identification, contact tracing and related measures
    2. Environmental measures
    3. Healthcare and public health capacity
    4. Resource allocation
    5. Returning to normal life
    6. Risk communication
    7. Social distancing
    8. Travel restriction

Then the specific description of each NPI is coded into category (L2), subcategory (L3) and code (L4) that describe the intervention onto subsequent levels of details.

The Master List of Codes provided on the Github shows all combinations of theme/category/subcategory/code used in the CCCSL dataset.

R Codes for exploring the CCCSL dataset are available at: https://doi.org/10.5281/zenodo.3949808 and https://github.com/amel-github/CCCSL-Codes

The dataset also provides the description of the measure as found in the text data source, translated into English (filed Comment). This field allows judging the quality of the label for the different levels of the coding scheme and enables to re-assign the measure to the correct theme/category/subcategory/code in case of error or misinterpretation by the data collector/data coder.
For purposes of transparency and to motivate collaborative validation process, we provide, for each NPI, the link to the information source, which enables i) to trace back potential changes in the meaning of the label during the translation and ii) to access the description of the measures in the source language and/or to access to the information as it was dispatched originally.
An open library of information sources is available via the free software Zotero (work in progress) that contains all sources used to collect the data: https://www.zotero.org/groups/2488884/cccsl_covid_measure_project.
The standardized coding scheme, together with the open library of information sources, enables a great flexibility of use of the data for diverse research questions.

Data access

The Data can be found at https://github.com/amel-github/covid19-interventionmeasures

The CCCSL dataset is continuously updated.

Cite as:

Desvars-Larrive A., Ahne V., Álvarez S., Bartoszek M., Berishaj D., Bulska D., Chakraborty A., Chen E., Chen X., Cserjan D., Dervic A., Dervic E., Di Natale A., Ferreira M.R., Flores Tames E., Garcia D., Garncarek Z., Gliga D.S., Gooriah L., Grzymała-Moszczyńska J., Jurczak A., Haberfellner S., Hadziavdic L., Haug N., Holder S., Korbel J., Lasser J., Lederhilger D., Niederkrotenthaler T., Pacheco A., Pocasangre-Orellana X.M., Reddish J., Reisch V., Roux A., Sorger J., Stangl J., Stoeger L., Takriti H., Ten A., Vierlinger R., Thurner S. CCCSL: Complexity Science Hub Covid-19 Control Strategies List (2020). Version 2.0. https://github.com/amel-github/covid19-interventionmeasures

Data use license

Creative Commons Share-Alike with Attribution: CC BY-SA 4.0.


Our paper has been published in Scientific Data: Desvars-Larrive, A., Dervic, E., Haug, N. et al.
A structured open dataset of government interventions in response to COVID-19. Sci Data 7, 285 (2020). https://doi.org/10.1038/s41597-020-00609-9

We are waiting for your feedback…

If you see any inaccuracies in the underlying data or if some NPIs are missing, please contact us via the form here.

The CCCSL is part of a worldwide initiative

Joining a global effort to fight the pandemic, our dataset has been integrated into the Global Dataset of Public Health and Social Measures (PHSM) aggregated by the World Health Organization (WHO): https://www.who.int/emergencies/diseases/novel-coronavirus-2019/phsm

Descriptive visualizations of the dataset




Other initiatives on the topic


Contact information

Amélie Desvars-Larrive (Complexity Science Hub Vienna)


EOSCsecretariat.eu has received funding from the European Union’s Horizon Programme call H2020-INFRAEOSC-05-2018-2019, grant Agreement number 831644. 


This work is coordinated by the Complexity Science Hub Vienna, Austria.
This work is supported by the University of Veterinary Medicine Vienna, Austria.

Country visualizations by Michael Gruber and Jana Lasser. Website by David Garcia and Michael Gruber.