COVID-19: An exploration of consecutive systemic barriers to pathogen-related data sharing during a pandemic

Abstract In 2020, the COVID-19 pandemic resulted in a rapid response from governments and researchers worldwide, but information-sharing mechanisms were variable, and many early efforts were insufficient for the purpose. We interviewed fifteen data professionals located around the world, working with COVID-19-relevant data types in semi-structured interviews. Interviews covered both challenges and positive experiences with data in multiple domains and formats, including medical records, social deprivation, hospital bed capacity, and mobility data. We analyze this qualitative corpus of experiences for content and themes and identify four sequential barriers a researcher may encounter. These are: (1) Knowing data exists, (2) being able to access that data, (3) data quality, and (4) ability to share data onwards. A fifth barrier, (5) human throughput capacity, is present throughout all four stages. Examples of these barriers range from challenges faced by single individuals to non-existent records of historic mingling/social distance laws, and up to systemic geopolitical data suppression. Finally, we recommend that governments and local authorities explicitly create machine-readable temporal “law as code” for changes in laws such as mobility/mingling laws and changes in geographical regions.