Wastewater monitoring of COVID-19: a perspective from Scotland.

Scotland introduced wastewater monitoring for COVID-19 early in the pandemic. From May 2020, samples have been taken and analysed using quantitative polymerase chain reaction (qPCR). The programme was expanded to over 100 sites accounting for around 80% of the population. Data are presented publicly via a dashboard and regular reports are produced for both the public and health professionals. Wastewater-based epidemiology (WBE) offers opportunities and challenges. It offers an objective means of measuring COVID-19 prevalence and can be more practical or timely than other methods of mass testing. However, it also has substantial variability impacted by multiple environmental factors. Methods for data collection and analysis have developed significantly through the pandemic, reflecting the evolving situation and policy direction. We discuss the Scottish experience of wastewater monitoring for COVID-19, with a focus on the analysis of data. This includes our approach to flow normalisation, our experience of variability in measurements and anomalous values, and the visualisation and presentation of data to stakeholders. Summarising the Scottish methodology as of March 2022, we also discuss how wastewater data were used for informing policy and public health actions. We draw lessons from our experience and consider future directions for WBE in Scotland.

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.

Understanding and managing uncertainty and variability for wastewater monitoring beyond the pandemic: Lessons learned from the United Kingdom national COVID-19 surveillance programmes.

The COVID-19 pandemic has put unprecedented pressure on public health resources around the world. From adversity, opportunities have arisen to measure the state and dynamics of human disease at a scale not seen before. In the United Kingdom, the evidence that wastewater could be used to monitor the SARS-CoV-2 virus prompted the development of National wastewater surveillance programmes. The scale and pace of this work has proven to be unique in monitoring of virus dynamics at a national level, demonstrating the importance of wastewater-based epidemiology (WBE) for public health protection. Beyond COVID-19, it can provide additional value for monitoring and informing on a range of biological and chemical markers of human health. A discussion of measurement uncertainty associated with surveillance of wastewater, focusing on lessons-learned from the UK programmes monitoring COVID-19 is presented, showing that sources of uncertainty impacting measurement quality and interpretation of data for public health decision-making, are varied and complex. While some factors remain poorly understood, we present approaches taken by the UK programmes to manage and mitigate the more tractable sources of uncertainty. This work provides a platform to integrate uncertainty management into WBE activities as part of global One Health initiatives beyond the pandemic.

Predicting a change in the order of spring phenology in temperate forests.

The rise in spring temperatures over the past half-century has led to advances in the phenology of many nontropical plants and animals. As species and populations differ in their phenological responses to temperature, an increase in temperatures has the potential to alter timing-dependent species interactions. One species-interaction that may be affected is the competition for light in deciduous forests, where early vernal species have a narrow window of opportunity for growth before late spring species cast shade. Here we consider the Marsham phenology time series of first leafing dates of thirteen tree species and flowering dates of one ground flora species, which spans two centuries. The exceptional length of this time series permits a rare comparison of the statistical support for parameter-rich regression and mechanistic thermal sensitivity phenology models. While mechanistic models perform best in the majority of cases, both they and the regression models provide remarkably consistent insights into the relative sensitivity of each species to forcing and chilling effects. All species are sensitive to spring forcing, but we also find that vernal and northern European species are responsive to cold temperatures in the previous autumn. Whether this sensitivity reflects a chilling requirement or a delaying of dormancy remains to be tested. We then apply the models to projected future temperature data under a fossil fuel intensive emissions scenario and predict that while some species will advance substantially others will advance by less and may even be delayed due to a rise in autumn and winter temperatures. Considering the projected responses of all fourteen species, we anticipate a change in the order of spring events, which may lead to changes in competitive advantage for light with potential implications for the composition of temperate forests.

Exploring relationships between phenological and weather data using smoothing.

Stepwise regression is often used to draw associations between phenological records and weather data. For example, the dates that a species first flowers each year might be regressed on monthly mean temperatures for a period preceding flowering. The months that 'best' explain the variation in first flowering dates would be selected by stepwise regression. However, daily records of weather are usually available. Stepwise regression on daily temperatures would not be appropriate because of high correlations between neighbouring days. Smoothing methods provide a way of avoiding such difficulties. Regression coefficients can be smoothed by penalising differences in slopes between neighbouring regressors. The resultant curve of regression gradients is intuitively attractive. Various possible approaches to smoothing regression coefficients are discussed. We illustrate the use of one method, P-spline signal regression, which is particularly appropriate when there are many more regressors than observations. Smoothing can be applied to more than one set of regressors. This results in a multi-dimensional surface of regression coefficients. We use this approach to investigate how the time of year that a plant species tends to flower affects its relationship with temperature records. Using this method, we found that later species tend to be affected by later temperatures.

Incidence and Distribution of Raspberry bushy dwarf virus in Commercial Red Raspberry (Rubus idaeus) Crops in Scotland.

A survey was done in 1998 to determine whether Raspberry bushy dwarf virus (RBDV) was established in raspberry fruiting plantations in Scotland. Raspberry-producing holdings were selected according to geographical area and size. Samples (201), each comprising 60 shoots per stock, were obtained from 77 holdings and tested by enzyme-linked immunosorbent assay (ELISA). ELISA-positive shoots from each infected stock were grafted onto cultivar Glen Clova, which is resistant to the Scottish-type isolate of RBDV (RBDV-S), to establish whether the virus is a resistance-breaking (RB) isolate. RBDV was detected in 22% of the stocks sampled, with 2 to 80% incidence of infection. No RBDV was in any of the 40 plantations containing cultivars resistant to RBDV-S or in Glen Clova plants, which were grafted successfully with samples from 15 infected plantations, indicating that no RB isolates were detected. The percentage of infected plantations increased with time from the planting date. In order to investigate possible sources of infection, ELISA for RBDV was made in 1999 on samples of stocks of raspberry cultivars entered for the lowest certified grade (Standard Grade) in Scotland and, in 1994 to 1997, on certified stocks planted with material originating from outside Scotland. No RBDV was detected in any of the samples. RBDV was found only rarely in samples of wild raspberry in Angus and Perthshire.