Open science and COVID-19: first open database on the interventions of every country in the world

To know which non-pharmaceutical interventions against the spread of the coronavirus have worked best, researchers and health authorities need data, and not being able to think of a future of closed borders, with those who would be further behind than others, sharing this data it is essential to think globally. In March, the Complexity Science Hub in Vienna launched a global collection of all the government measures that have been implemented in recent months to combat the pandemic. The first results were published on August 27, 2020 in an article in the journal Nature Scientific Data and can be freely navigated on the CSH COVID-19 CONTROL STRATEGIES LIST (CCCSL) platform.

This is information on 6,068 interventions from 56 countries: 33 European, 12 Asian, 5 South American, 2 North American, 3 African (only) and one ocean, plus the Diamond Princess cruise ship. For the United States, data was collected at the state level for 24 states in the United States. Also included are measures implemented at subnational level (details of state, region, city). The intention is to further update this data set until the end of December 2020.

The data was collected from public sources, including official government sources, scientific articles, press releases, government communications and social media. All records have been hand-coded and are updated regularly. The CCCSL provides the date of implementation of the intervention, and when this is missing, it uses the date of the announcement of the entry into force of that intervention.

How to read the interactive map

The scientists divided the measurements into 8 groups (L1, in green):

• case identification, contact tracing and relative isolation measures;
• environmental measures including disinfection and cleaning of common surfaces (public transport, markets);
• sanitary and sanitary capacity (for example increase of hospital capacity or healthcare personnel);
• allocation of resources (such as the operations involved in the allocation of budgets, in the use of resources and in the distribution of goods for the control of the epidemic);
• risk communication;
• reduction of contacts;
• travel limitation;
• "return to normal life".

Each type of intervention in the eight groups is codified on four levels, depending on how specific the action was. “For example for the category (L1) of travel restrictions there are seven sub-categories (L2), which are further divided into more than 50 sub-categories (L3).

Just click on one of the green dots (L1) and the interactive map will show the sub-categories through which this intervention has been declined and in which countries.

An open library of information sources is available through the free Zotero software (work in progress) which contains all the sources used to collect the data: https://www.zotero.org/groups/2488884/cccsl_covid_measure_project.

But the most interesting results are yet to come. "We quantified the impact of individual control policies and compiled a ranking based on their effectiveness in reducing the spread of the coronavirus," explains Amelie Desvars-Larrive, project leader and first author of the article. These findings are currently under review.

“The special value of our dataset is its granularity,” says Amelie Desvars-Larrive. "To our knowledge, it is the most detailed classification of government measures against COVID-19 to date." It should be noted that during the crisis, other research groups also tried to track data on government policies. These data are integrated into the World Health Organization's Tracking Public Health and Social Measures (PHSM), a global database of health and social measures that have been deployed during the COVID-19 pandemic by individuals, institutions, communities. , local and national governments and international bodies.

"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."