Dynamic population normalisation in wastewater-based epidemiology for improved understanding of the SARS-CoV-2 prevalence: a multi-site study.

Wastewater-based epidemiology (WBE) is a valuable tool for monitoring the circulation of COVID-19. However, while variations in population size are recognised as major sources of uncertainty, wastewater SARS-CoV-2 measurements are not routinely population-normalised. This paper aims to determine whether dynamic population normalisation significantly alters SARS-CoV-2 dynamics observed through wastewater monitoring, and whether it is beneficial or necessary to provide an understanding of COVID-19 epidemiology. Data from 394 sites in England are used, and normalisation is implemented based on ammoniacal nitrogen and orthophosphate concentrations. Raw and normalised wastewater SARS-CoV-2 metrics are evaluated at the site and spatially aggregated levels are compared against indicators of prevalence based on the Coronavirus Infection Survey and Test and Trace polymerase chain reaction test results. Normalisation is shown, on average, to have a limited impact on overall temporal trends. However, significant variability in the degree to which it affects local-level trends is observed. This is not evident from previous WBE studies focused on single sites and, critically, demonstrates that while the impact of normalisation on SARS-CoV-2 trends is small on average, this may not always be the case. When averaged across many sites, normalisation strengthens the correlation between wastewater SARS-CoV-2 data and prevalence indicators; however, confidence in the improvement is low.

Research needs for optimising wastewater-based epidemiology monitoring for public health protection.

Wastewater-based epidemiology (WBE) is an unobtrusive method used to observe patterns in illicit drug use, poliovirus, and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The pandemic and need for surveillance measures have led to the rapid acceleration of WBE research and development globally. With the infrastructure available to monitor SARS-CoV-2 from wastewater in 58 countries globally, there is potential to expand targets and applications for public health protection, such as other viral pathogens, antimicrobial resistance (AMR), pharmaceutical consumption, or exposure to chemical pollutants. Some applications have been explored in academic research but are not used to inform public health decision-making. We reflect on the current knowledge of WBE for these applications and identify barriers and opportunities for expanding beyond SARS-CoV-2. This paper critically reviews the applications of WBE for public health and identifies the important research gaps for WBE to be a useful tool in public health. It considers possible uses for pathogenic viruses, AMR, and chemicals. It summarises the current evidence on the following: (1) the presence of markers in stool and urine; (2) environmental factors influencing persistence of markers in wastewater; (3) methods for sample collection and storage; (4) prospective methods for detection and quantification; (5) reducing uncertainties; and (6) further considerations for public health use.

An analysis of 45 large-scale wastewater sites in England to estimate SARS-CoV-2 community prevalence.

Accurate surveillance of the COVID-19 pandemic can be weakened by under-reporting of cases, particularly due to asymptomatic or pre-symptomatic infections, resulting in bias. Quantification of SARS-CoV-2 RNA in wastewater can be used to infer infection prevalence, but uncertainty in sensitivity and considerable variability has meant that accurate measurement remains elusive. Here, we use data from 45 sewage sites in England, covering 31% of the population, and estimate SARS-CoV-2 prevalence to within 1.1% of estimates from representative prevalence surveys (with 95% confidence). Using machine learning and phenomenological models, we show that differences between sampled sites, particularly the wastewater flow rate, influence prevalence estimation and require careful interpretation. We find that SARS-CoV-2 signals in wastewater appear 4-5 days earlier in comparison to clinical testing data but are coincident with prevalence surveys suggesting that wastewater surveillance can be a leading indicator for symptomatic viral infections. Surveillance for viruses in wastewater complements and strengthens clinical surveillance, with significant implications for public health.

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.

Building knowledge of university campus population dynamics to enhance near-to-source sewage surveillance for SARS-CoV-2 detection.

Wastewater surveillance has been widely implemented for monitoring of SARS-CoV-2 during the global COVID-19 pandemic, and near-to-source monitoring is of particular interest for outbreak management in discrete populations. However, variation in population size poses a challenge to the triggering of public health interventions using wastewater SARS-CoV-2 concentrations. This is especially important for near-to-source sites that are subject to significant daily variability in upstream populations. Focusing on a university campus in England, this study investigates methods to account for variation in upstream populations at a site with highly transient footfall and provides a better understanding of the impact of variable populations on the SARS-CoV-2 trends provided by wastewater-based epidemiology. The potential for complementary data to help direct response activities within the near-to-source population is also explored, and potential concerns arising due to the presence of heavily diluted samples during wet weather are addressed. Using wastewater biomarkers, it is demonstrated that population normalisation can reveal significant differences between days where SARS-CoV-2 concentrations are very similar. Confidence in the trends identified is strongest when samples are collected during dry weather periods; however, wet weather samples can still provide valuable information. It is also shown that building-level occupancy estimates based on complementary data aid identification of potential sources of SARS-CoV-2 and can enable targeted actions to be taken to identify and manage potential sources of pathogen transmission in localised communities.

Seasonal influences on the use of genetic markers as performance indicators for small wastewater treatment plants.

The development of microbial source tracking methods has resulted in an array of genetic faecal markers for assessing human health risks posed from surface water pollution. However, their use as performance metrics at wastewater treatment plants (WWTPs) has not been explored extensively. Here we compared three Bacteroides (HF183, HumM2, AllBac) and two E. coli (H8, RodA) genetic markers for summer and winter performance monitoring at twelve small rural (<250 PE) and three larger WWTPs in NE England. Small WWTPs are of interest because they are poorly understood and their impact on surface water quality may be underestimated. Overall, genetic marker data showed significant differences in treatment performance at smaller versus larger WWTPs. For example, effluent abundances of HF183 and HumM2 were significantly higher in smaller systems (p = 0.003 for HumM2; p = 0.02 for HF183). Genetic markers also showed significant differences in performance between seasons (p < 0.01, n = 120), with human-specific markers (i.e., HF183, HumM2, H8) being generally better for summer WWTP monitoring. In contrast, Bacteroides markers were much more suitable for winter monitoring, possibly because the E. coli markers are less sensitive to differences in temperature and sunlight conditions. Overall, Bacteroides markers best described WWTP treatment performance across all samples, although seasonal differences suggest caution is needed when markers are used for performance monitoring. Genetic markers definitely provide rapid and new information about WWTP performance, but more spatially diverse studies are needed to refine their use for routine WWTP monitoring.

Co-optimization of sponge-core bioreactors for removing total nitrogen and antibiotic resistance genes from domestic wastewater.

Inadequate sanitation can lead to the spread of infectious diseases and antimicrobial resistance (AMR) via contaminated water. Unfortunately, wastewater treatment is not universal in many developing and emerging countries, especially in rural and peri-urban locations that are remote from central sewers. As such, small-scale, more sustainable treatment options are needed, such as aerobic-Denitrifying Downflow Hanging Sponge (DDHS) bioreactors. In this study, DDHS reactors were assessed for such applications, and achieved over 79% and 84% removal of Chemical Oxygen Demand and Ammonium, respectively, and up to 71% removal of Total Nitrogen (TN) from domestic wastes. Elevated TN removals were achieved via bypassing a fraction of raw wastewater around the top layer of the DDHS system to promote denitrification. However, it was not known how this bypass impacts AMR gene (ARG) and mobile genetic element (MGE) levels in treated effluents. High-throughput qPCR was used to quantify ARG and MGE levels in DDHS bioreactors as a function of percent bypass (0, 10, 20 and 30% by volume). All systems obtained over 90% ARG reduction, although effluent ARG and TN levels differed among bypass regimes, with co-optimal reductions occurring at ~20% bypass. ARG removal paralleled bacterial removal rate, although effluent bacteria tended to have greater genetic plasticity based on higher apparent MGE levels per cell. Overall, TN removal increased and ARG removal decreased with increasing bypass, therefore co-optimization is needed in each DDHS application to achieve locally targeted TN and AMR effluent levels.