After weeks of increasing infections due to covid-19 , the Dutch government announced on 12 March 2020 that the Netherlands would go into a lockdown. A real sense of urgency arose when frightening images emerged of Italian hospitals at breaking point due to a high number of covid patients. This unprecedented situation caused an immediate feeling of great uncertainty about what this meant for Dutch society and the economy. Statistics Netherlands tried to contribute to reducing the uncertainty through the introduction of new types of output dedicated to the covid-19 crisis, mainly disseminated via its own website in the form of dashboards that described important aspects of the covid-19 crisis. In part, these were based on combining output from existing statistics based on register data around themes that had become relevant because of the crisis (see the website). Other parts of these dashboards were produced by process or product innovation, such as the introduction of new breakdowns and aggregates, speeding up production and release, increasing the frequency, accessing new data sources and the development of new kinds of output.
Dashboards were an important way to disseminate the data. A first important example described the medical consequences of the epidemic in the Netherlands, including the number of deaths, sickness leave at work, the effect on life expectancy and the pressure on medical care. Another one showed the social consequences of the crisis, including data on criminality, overnight stays in hotels and other lodging facilities and the development of the number of asylum requests by refugees. Other dashboards focused on the economic effects, changes in mobility patterns, consequences on employment and income, the development of government finances and regional differences.
3.2. Increased timeliness and frequency
Existing indicators that appeared in the dashboard were published in a more timely manner such as the monthly figures on retail sales, which were published two weeks earlier. In other cases, the timeliness and frequency of statistics was increased. The monthly figures on mortality were published on a weekly basis, after a quick adaptation of the production process. Also, mortality figures were linked to other administrative data such as benefits for long-term medical care. Furthermore, comparing mortality figures with figures of the past years, estimates were published on excess mortality as an indication for the effect of the corona pandemic broken down by age groups. For these figures Statistics Netherlands worked closely with The National Institute for Public Health and Environment (RIVM). Another example is the introduction of weekly instead of monthly figures on firms’ bankruptcies. This was made possible by an adaptation of the statistical production process.
3.3. New outputs
In some cases, new outputs were made possible due to access to new data sources. One example is the publication of weekly data on various types of payment transactions, together with an external party (the Dutch payments association ‘Betaalvereniging Nederland’). A second example consists of weekly figures on check-ins in public transportation, together with Translink. The same data sources also allowed analysis of changes in the use of transportation during the day. These innovations were possible by using data from digital production systems from other companies and institutions. These production systems basically operate in real-time and allow high frequent and timely statistical results. A third example is the help Statistics Netherlands could offer in sewage analysis in the covid crisis together with RIVM. Because of covid-19, sewage treatment plants in The Netherlands test for RNA traces of covid-19 at least once a week. By combining the results with demographic data virus spread based on sewage data could be published on a more the local level.
Also a number of potentially new outputs were investigated using for example social media data. This resulted in (online) brainstorm sessions of statistical and Big Data experts which aimed to identify data sources with timely available data that had the potential to fill the need for indicators on new phenomena. After identifying these sources, which often included social media and web data, short exploratory studies were performed to determine to what extent these sources were able to cover the information demand. Successful examples of these studies are: social media to detect users with corona-related symptoms (De Broe et. all., 2021b) and social media and web posts to detect changes in the attitude(s) towards vaccination (work in progress). However, not every idea succeeded. An example of the latter was a study with the aim to predict the potential effect of the corona crisis on the birth rate in the Netherlands. In this study, social media and discussions on webfora were scraped to determine if and how often people (usually woman) posted that they were pregnant, including the number of weeks, and the estimated date of birth. Here, it was found that limited data was available and that the trend of posting this information online decreased over time during the years for which the data was available (from 2013 onwards). Because of this, no reliable indicator could be developed for this phenomena. One idea which remains to be investigated is the sale of folic acid in scanner data as an indicator of pregnancy and early indicator of births.