COVID-19 case data provide an interesting illustration of the importance of the interactions between features. Immense value has been derived from the presence of some of the 12 features but constrained by the absence of other features. This timeliness and frequency of the data has allowed countries to compare the possible trajectories they are likely to face and scale their emergency response, social distancing measures, and economic recovery packages accordingly. The case data are often tied to specific geographical locales and to an individual for whom other relevant data, such as age and medical history, are available. This granularity has made it possible to understand how clusters of cases evolved and how mortality rates differ by individual circumstances and characteristics. This has allowed governments to take extra measures for people who are suffering from or are vulnerable to the virus. The accessibility of the data has enabled quick research on the topic, providing the evidence policy makers need when deciding what measures to implement.

While these features have helped shape measures to contain and limit the spread and consequences of the virus, the measures could have improved had the quality of the data been better. In particular, questions about data accuracy and cross-country comparability have led to concerns about their relevance for policy making. As countries have increased their testing capabilities over time, more people have been reported as having contracted the virus. Though increased testing is critical, this has made data on confirmed cases less comparable over time within countries. Data on deaths suffer from cross-country comparability problems, given that the definition of deaths caused by COVID-19 differs across countries, challenging models using cross-country data.

COVID-19 case data also contain cases of positive and negative spillovers between features. On the positive side, the intensive data use during the pandemic will undoubtedly have an impact on the value attached to having timely and accurate data in other domains going forward. On the other hand, as we will argue in more detail later in the paper, the granularity and timeliness of the data have increased the risk of government surveillance for ends beyond just containing the virus.

# **Relation to other frameworks**

The 12 attributes relate closely to other frameworks that assess the quality of statistical data. Such frameworks have primarily been created by National Statistical Offices (see for example Statistics Canada (2017)), international organizations (see for example OECD (2011) and UN (2019)), and academia (see for example Biemer (2010)). Table 1 compares the framework derived in this paper with these other frameworks.

Unsurprisingly, there is a great overlap between the different frameworks, though the exact names and definitions differ from source to source. All 12 features we identify can be found in some shape or form in one of the other frameworks. Though many of the frameworks originate from work with survey data, they can be adapted to other data types. The framework of Biemer (2010), for example, which is based on the concept of a total survey error, has been adapted to big data (Amaya et al. 2020) and agricultural data (Carletto et al. 2021).

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