Journal of Data Science

To surveil the development of COVID-19 is a complex and challenging issue. The foundation of such surveillance is timely and accurate epidemic data. Therefore, quality control for releasing COVID-19 data is very important, accounting for the releasing agent, the content to release, and the impact of the released data. We suggest that the quality requirements for the release of COVID-19 data be based on the global perspective that the goal of open epidemic data is to create a valuable ecological chain in which all stakeholders are involved. As such, the collection, aggregation, and release process of the COVID-19 data should meet not only the data quality standards of official statistics and health statistics, but also the characteristics of the epidemic statistics and the needs of pandemic prevention. The quality requirements should follow the unique characteristics of the epidemic and be scrutinized by the public. We integrate the perspectives of official statistics, health statistics, and open government data, proposing five quality dimensions for releasing COVID-19 data: accuracy, timeliness, systematicness, userfriendliness and security. Through case studies on the official websites of Chinese provincial health commission, we report the quality problems in the current data releasing process and suggest improvements.