Not going to waste - preserving Scotland's COVID-19 wastewater data

Poster presented at the Edinburgh Open Research Conference 2022

Particles of the SARS-CoV-2 virus, the causative agent of the COVID-19 disease, can enter the sewage system through faeces of infected people. The Scottish Environment Protection Agency (SEPA) has been monitoring viral levels in wastewater since May 2020 in over 100 locations, as such data is a good indicator of COVID-19 spread throughout the population. These estimates are combined with others to provide the best overall estimate of disease prevalence and viral reproduction (R) values, at the Scottish and UK levels. These longitudinal, geospatial data are costly to obtain while they have a high potential for re-use. However, access to the data risks deteriorating over time once COVID-19 becomes an endemic infection and monitoring programmes terminate. Our team worked on the open research front of the programme monitoring SARS-CoV-2 in wastewater, with the aim to develop appropriate preservation strategies while following open science and FAIR data principles. Here we will present the curation process of making the outputs complete and unambiguous. We will describe the multiple ways in which we disseminated the data to maximize their visibility and re-usability, while assuring cost-free, long-term preservation, as well as share some recommendations for such multi-institutional initiatives. Briefly, our work included sharing data in public repositories, submitting a data paper to a scientific data journal, and curating and transcribing protocols, which were also published in online platforms. A dashboard webpage containing the links to published outputs was also created: https://biordm.github.io/COVID-Wastewater-Scotland. By making these data open and FAIR, we are supporting COVID-19 data transparency, assisting with government decisions and accountability. Additionally, the detailed data and methodologies can help with the implementation of similar surveillance programmes in the future, as well as assist with the modelling and analysis of the past SARS-CoV-2 outbreaks.