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

• URL: http://arxiv.org/abs/2205.12098v3
• Date: Fri, 22 Dec 2023 12:23:02 GMT
• Title: COVID-19: An exploration of consecutive systemic barriers to pathogen-related data sharing during a pandemic
• Authors: Yo Yehudi, Lukas Hughes-Noehrer, Carole Goble and Caroline Jay
• Abstract summary: 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. Data professionals working with pandemic-relevant data often face significant systemic barriers to accessing, sharing or re-using this data.
• Score: 3.192308005611312
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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