SARS-CoV-2 RNA levels in Scotland’s wastewater

Nationwide, wastewater-based monitoring was newly established in Scotland to track the levels of SARS-CoV-2 viral RNA shed into the sewage network, during the COVID-19 pandemic. We present a curated, reference dataset produced by this national programme, from May 2020 to February 2022. Viral levels were analysed by RT-qPCR assays of the N1 gene, on RNA extracted from wastewater sampled at 162 locations. Locations were sampled up to four times per week, typically once or twice per week, and in response to local needs. We report sampling site locations with geographical coordinates, the total population in the catchment for each site, and the information necessary for data normalisation, such as the incoming wastewater flow values and ammonia concentration, when these were available. The methodology for viral quantification and data analysis is briefly described, with links to detailed protocols online. These wastewater data are contributing to estimates of disease prevalence and the viral reproduction number (R) in Scotland and in the UK

Typing myalgic encephalomyelitis by infection at onset: A DecodeME study [version 4; peer review: 2 approved]

Background: People with myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) experience core symptoms of post-exertional malaise, unrefreshing sleep, and cognitive impairment. Despite numbering 0.2-0.4% of the population, no laboratory test is available for their diagnosis, no effective therapy exists for their treatment, and no scientific breakthrough regarding pathogenesis has been made. It remains unknown, despite decades of small-scale studies, whether individuals experience different types of ME/CFS separated by onset-type, sex or age. Methods: DecodeME is a large population-based study of ME/CFS that recruited 17,074 participants in the first 3 months following full launch. Detailed questionnaire responses from UK-based participants who all reported being diagnosed with ME/CFS by a health professional provided an unparalleled opportunity to investigate, using logistic regression, whether ME/CFS severity or onset type is significantly associated with sex, age, illness duration, comorbid conditions or symptoms. Results: The well-established sex-bias among ME/CFS patients is evident in the initial DecodeME cohort: 83.5% of participants were females. What was not known previously was that females tend to have more comorbidities than males. Moreover, being female, being older and being over 10 years from ME/CFS onset are significantly associated with greater severity.  Five different ME/CFS onset types were examined in the self-reported data: those with ME/CFS onset (i) after glandular fever (infectious mononucleosis); (ii) after COVID-19 infection; (iii) after other infections; (iv) without an infection at onset; and, (v) where the occurrence of an infection at or preceding onset is not known. Among other findings, ME/CFS onset with unknown infection status was significantly associated with active fibromyalgia. Conclusions: DecodeME participants differ in symptoms, comorbid conditions and/or illness severity when stratified by their sex-at-birth and/or infection around the time of ME/CFS onset

Not Going to Waste - Preserving Scotland’s COVID-19 Wastewater Data

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. Funding: CREW; Grant CD2019_06 Tracking SARS-CoV-2 via municipal wastewate