  In 2020, the COVID-19 pandemic resulted in a rapid response from governments
and researchers worldwide. As of late 2023, over millions have died as a result
of COVID-19, with many COVID-19 survivors going on to experience long-term
effects weeks, months, or years after their illness. Despite this staggering
toll, those who work with pandemic-relevant data often face significant
systemic barriers to accessing, sharing or re-using this data. In this paper we
report results of a study, where we interviewed data professionals working with
COVID-19-relevant data types including social media, mobility, viral genome,
testing, infection, hospital admission, and deaths. These data types are
variously used for pandemic spread modelling, healthcare system strain
awareness, and devising therapeutic treatments for COVID-19. Barriers to data
access, sharing and re-use include the cost of access to data (primarily
certain healthcare sources and mobility data from mobile phone carriers), human
throughput bottlenecks, unclear pathways to request access to data,
unnecessarily strict access controls and data re-use policies, unclear data
provenance, inability to link separate data sources that could collectively
create a more complete picture, poor adherence to metadata standards, and a
lack of computer-suitable data formats.
