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CCCSL: Complexity Science Hub COVID-19 Control Strategies List

Update 2021-03-01

Objective of the Project
Note on the Data
Data Sources
CCCSL Fields
Master List of Codes
Glossary of Codes
R Codes
List of Contributors


During the COVID-19 pandemic, governments have enforced a broad spectrum of interventions, under rapidly changing, unprecedented circumstances. Public health and social measures (PHSMs), also called non-pharmaceutical interventions (NPIs), aim to prevent the introduction of infectious diseases, control their spread, and reduce their burden on the health system. The general concept of containing the initial (exponential) spread of a disease is called “flattening the (epi-)curve”. By reducing the growth rate of an epidemic, PHSMs reduce the stress on the healthcare system and help gaining time to develop and produce vaccines and specific medications, which is of utmost importance in the case of emerging infectious diseases.

Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic and its impacts, e.g. on the society, the economy, public health, or human rights.

Objective of the Project

Started in mid-March 2020, our project aims to generate a comprehensive structured dataset on government responses to COVID-19, including the respective time schedules of their implementation.

Data types
Our dataset presents PHSMs but also economic measures (EMs) implemented in response to COVID-19.

The dataset is readily usable for modelling and machine learning analyses and exhibits a great analytical flexibility.
We also provide user-friendly documentation and materials (codes, visualisation interface, and library of sources) along with the dataset, which allow a maximum understanding of the data and promote its use among non-experts.


Our methodology is published: Desvars-Larrive, A., Dervic, E., Haug, N. et al. A structured open dataset of government interventions in response to COVID-19. Scientific Data 7, 285 (2020).

Note on the Data

This project is an emergency research project started in response to the COVID-19 health crisis. The data has been collected in a very limited time. This dataset is dynamic, continuously updated, and is subjected to continuous data validation and code curation process.

Data Sources

A wide range of different public sources are used to populate, update and curate our dataset, including official government sources, peer-reviewed and non-peer-reviewed scientific papers, webpages of public health institutions (WHO, CDC, and ECDC), press releases, newspaper articles, and government communication through social media.

An Open Library of Sources is available that contains all sources used to collect the data:
We also provide various formats of this library that can be imported in different reference manager software.

CCCSL Fields

id – Unique identifier for the implemented measure.
Country – The country where the measure was implemented.
ISO3 – Three-letter country code as published by the International Organization for Standardization.
State – Subnational geographic area. State where the measure was implemented; the country name otherwise. Used for Germany, India, UK, and USA.
Region – Subnational geographic area (e.g. region, department, municipality, city) where the measure has been locally implemented (i.e. the measure was not implemented nationwide as of the mentioned date). The country or the state name otherwise (i.e. measure implemented nationwide).
Date – Date of implementation of the measure. Date of announcement was used when the date of implementation of the measure could not be found and this was specified in the field Comment.
L1_Measure – Theme (L1 of the classification scheme). Eight themes were defined:
(1) Case identification, contact tracing and related measures,
(2) Environmental measures,
(3) Healthcare and public health capacity,
(4) Resource allocation,
(5) Risk communication,
(6) Social distancing,
(7) Travel restriction,
(8) Returning to normal life.
L2_Measure – Category (L2 of the classification scheme). See Description_changes_v2_20201216.pdf document for the list of the categories.
L3_Measure – Subcategory (L3 of the classification scheme). Provides detailed information on the corresponding category (L2).
L4_Measure – Code (L4 of the classification scheme). Corresponds to the finest level of description of the measure.
Status – Specifies whether the measure is a prolongation of a previously implemented measure (“Extended”) or a new measure (“New”). When this information has not been collected, the cell is empty.
Comment – Provides the description of the measure as found in the text source, translated into English.
Source – Provides the reference(s) for each recorded measure.

Master List of Codes

The Master List of Codes is a dynamic document, updated together with the dataset, as inductive codes emerge from the text source.

The CCCSL taxonomy can be visualized via our online interactive tool (Author: Sorger J.).

Glossary of Codes

The Glossary of Codes provides a definition for each code used to describe COVID-19 PHSMs and EMs in the CCCSL.

On 2020-12-16 (commit cd396b3) we have updated the CCCSL with an improved taxonomy (Categories/Subcategories/Codes). The most important change concerns the classification of gatherings (theme: Social distancing). Previous versions discriminated between small (< 50 persons) and mass gatherings (> 50 persons). However, this was not accurate enough with regard to closure of restaurants, shops, short-term accommodations, or businesses for which we did not know the capacity. We also wanted to adapt the codes with regard to several PHSMs, e.g. mask wearing policies and phase-out measures, which, worldwide, discriminate outdoor and indoor settings. Therefore, the theme “Social distancing” has new categories “Indoor gathering restriction”, “Outdoor gathering restriction” and “Indoor and outdoor gathering restriction”. See our Glossary of Codes for more details.

R Codes

R codes for exploring the dataset and reproducing the figures of our publication as well as some graphs displayed on our webpage are available on GitHub and Zenodo.


This project is licensed under the CC BY-SA 4.0 License - see the CC BY-SA 4.0 file for details.


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

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


Amélie Desvars-Larrive (Complexity Science Hub Vienna, Austria / University of Veterinary Medicine Vienna, Austria).

List of Contributors (alphabetical order)

Ahne Verena (Complexity Science Hub Vienna)
Álvarez Francisco S. (Fundación Naturaleza El Salvador)
Bartoszek Marta (Institute of Psychology, Jagiellonian University, Kraków, Poland / University of Veterinary Medicine Vienna)
Berishaj Dorontinë (Independent Scholar)
Bulska Dominika (Institute for Social Studies, University of Warsaw, Poland)
Chakraborty Abhijit (Complexity Science Hub Vienna)
Chen Jiaying (Section for Science of Complex Systems, Medical University of Vienna / Complexity Science Hub Vienna)
Chen Xiao (Independent Scholar)
Cserjan David (Complexity Science Hub Vienna)
Dervic Alija (Institute of Electrodynamics, Microwave and Circuit Engineering, Vienna University of Technology, Austria)
Dervic Elma (Section for Science of Complex Systems, Medical University of Vienna / Complexity Science Hub Vienna)
Desvars-Larrive Amélie (University of Veterinary Medicine Vienna / Complexity Science Hub Vienna)
Di Natale Anna (Section for Science of Complex Systems, Medical University of Vienna / Complexity Science Hub Vienna)
El Goukhi Jasmin (University of Vienna / University of Veterinary Medicine Vienna)
Ferreira Marcia R. (Complexity Science Hub Vienna)
Flores Tames Erwin (Complexity Science Hub Vienna)
Garcia David (Section for Science of Complex Systems, Medical University of Vienna / Complexity Science Hub Vienna)
Garncarek Zuzanna (Institute of Psychology, Jagiellonian University, Kraków, Poland)
Gliga Diana S. (Independent Scholar)
Gooriah Leana (German Centre for Integrative Biodiversity Research (iDiv), Leipzig, Germany)
Gruber Michael (University of Veterinary Medicine)
Grzymała-Moszczyńska Joanna (Institute of Psychology, Jagiellonian University, Kraków, Poland)
Jurczak Anna (Institute of Psychology, Jagiellonian University, Kraków, Poland)
Haberfellner Simon (Independent Scholar)
Hadziavdic Lamija (Independent Scholar)
Haug Nils (Section for Science of Complex Systems, Medical University of Vienna / Complexity Science Hub Vienna, Austria)
Holder Samantha (Independent Scholar)
Korbel Jan (Section for Science of Complex Systems, Medical University of Vienna / Complexity Science Hub Vienna)
Lasser Jana (Section for Science of Complex Systems, Medical University of Vienna / Complexity Science Hub Vienna)
Lederhilger Diana (Independent Scholar)
Niederkrotenthaler Thomas (Unit Suicide Research & Mental Health Promotion, Medical University of Vienna, Austria / Complexity Science Hub Vienna)
Pacheco Andrea (German Centre for Integrative Biodiversity Research (iDiv), Leipzig, Germany)
Pocasangre-Orellana Xochilt María (Fundación Naturaleza El Salvador)
Reddish Jenny (Seshat: The Global History Databank / Complexity Science Hub Vienna)
Reisch Viktoria (Independent Scholar)
Roux Alexandra (CERMES3, Ecole des Hautes Etudes en Sciences Sociales, Villejuif / Gender, Sexuality, Health, CESP, INSERM, Paris-Saclay University, Villejuif, France)
Schueller William (Complexity Science Hub Vienna)
Sorger Johannes (Complexity Science Hub Vienna)
Stangl Johannes (Independent Scholar)
Stoeger Laura (Complexity Science Hub Vienna)
Takriti Huda (Complexity Science Hub Vienna)
Ten Alexandr (Flowers project-team, National Research Institute for Digital Sciences (INRIA), Talence, France)
Thurner Stefan (Section for Science of Complex Systems, Medical University of Vienna / Santa Fe Institute, Santa Fe, USA / Complexity Science Hub Vienna)
Vierlinger Rainer (Independent Scholar)