April 6, 2020
Update on the
CCCSL: CSH Covid-19 Control Strategies List
Thank you, David Garcia, for setting this up!
The page provides
- a short description of the project
- the countries in our database (this is work in progress and will be updated regularly)
- some first graphs derived from the CCCSL database
- a link to the form for people who would like to help us completing or correcting the database
April 2, 2020
Largest dataset of governmental measures against the spreading of COVID-19 now freely available
The Complexity Science Hub Vienna publishes the CCCSL: CSH Covid-19 Control Strategies List. This to date most comprehensive dataset on governmental measures against Covid-19 spreading is a Herculean effort of about 30 people. We have identified more than 170 governmental measures and collected the implementation of these measures in 41 countries (more to come!), including the Diamond Princess. The CCCSL will be continually updated and can be freely used for scientific analysis.
“The CSH Covid-19 Control Strategies List (CCCSL) is a huge effort,” says project leader Amélie Desvars-Larrive. “So far it is the most comprehensive dataset on governmental measures against Covid-19. Of course we will continually update the list and add new countries”.
The Hub invites scientists around the world to download the dataset for their own analysis.
The CCCSL is currently available via https://github.com/amel-github/covid19-interventionmeasures
“We also welcome any help for completion and updates,” adds Amélie.
Those interested in helping us to complete the CCCSL will find soon a form and further guidance on our webpage.
Our researcher Jana Lasser plotted the measures implemented by the Austrian government:
About the CSH
The Complexity Science Hub Vienna was founded with the aim of using Big Data for the benefit of society. Among other things, the CSH systematically and strategically prepares large data sets so that they can be used in agent-based models. These simulations allow the effects of decisions in complex situations to be tested in advance and systematically assessed. Thus, the CSH provides fact-based foundations for an evidence-based governance.
N. Cao, M. Meyer, L. Thiele, O. Saukh
Pollen video library for benchmarking detection, classification, tracking and novelty detection tasks: dataset
in: DATA '20: Proceedings of the Third Workshop on Data: Acquisition To Analysis. Association for Computing Machinery, NY (2020) 23–25
Privacy-preserving machine learning for time series data
in: SenSys '20: The 18th ACM Conf on Embedded Networked Sensor Systems (2020) 813–814
F. Papst, N. Stricker, O. Saukh
Localization from activity sensor data: poster abstract
in: SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems (2020) 703–704
J. Lasser, et al.
Agent-based simulations for optimized prevention of the spread of SARS-CoV-2 in nursing homes
[submitted Nov. 19, 2020]