Age-dependent association between SARS-CoV-2 cases reported by passive surveillance and viral load in wastewater

Identification of protein biomarkers in wastewater linked to the incidence of COVID-19

Wastewater-based epidemiological (WBE) surveillance is a viable disease surveillance technique capable of monitoring the spread of infectious disease agents in sewershed communities. In addition to detecting viral genomes in wastewater, WBE surveillance can identify other endogenous biomarkers that are significantly elevated and excreted in the saliva, urine, and/or stool of infected individuals. Human protein biomarkers allow the realization of real-time WBE surveillance using highly sensitive biosensors. In this study, we analyzed endogenous protein biomarkers present in wastewater influent through liquid chromatography-tandem mass spectrophotometry and scaffold data-independent acquisition to identify candidate target protein biomarkers for WBE surveillance of SARS-CoV-2. We found that out of the 1382 proteins observed in the wastewater samples, 44 were human proteins associated with infectious diseases. These included immune response substances such as immunoglobulins, cytokine-chemokines, and complements, as well as proteins belonging to antimicrobial and antiviral groups. A significant correlation was observed between the intensity of human infectious disease-related protein biomarkers in wastewater and COVID-19 case numbers. Real-time WBE surveillance using biosensors targeting immune response proteins, such as antibodies or immunoglobulins, in wastewater holds promise for expediting the implementation of relevant policies for the effective prevention of infectious diseases in the near future.

Estimating rates of change to interpret quantitative wastewater surveillance of disease trends

BACKGROUND

Wastewater monitoring data can be used to estimate disease trends to inform public health responses. One commonly estimated metric is the rate of change in pathogen quantity, which typically correlates with clinical surveillance in retrospective analyses. However, the accuracy of rate of change estimation approaches has not previously been evaluated.

OBJECTIVES

We assessed the performance of approaches for estimating rates of change in wastewater pathogen loads by generating synthetic wastewater time series data for which rates of change were known. Each approach was also evaluated on real-world data.

METHODS

Smooth trends and their first derivatives were jointly sampled from Gaussian processes (GP) and independent errors were added to generate synthetic viral load measurements; the range hyperparameter and error variance were varied to produce nine simulation scenarios representing different potential disease patterns. The directions and magnitudes of the rate of change estimates from four estimation approaches (two established and two developed in this work) were compared to the GP first derivative to evaluate classification and quantitative accuracy. Each approach was also implemented for public SARS-CoV-2 wastewater monitoring data collected January 2021-May 2023 at 25 sites in North Carolina, USA.

RESULTS

All four approaches inconsistently identified the correct direction of the trend given by the sign of the GP first derivative. Across all nine simulated disease patterns, between a quarter and a half of all estimates indicated the wrong trend direction, regardless of estimation approach. The proportion of trends classified as plateaus (statistically indistinguishable from zero) for the North Carolina SARS-CoV-2 data varied considerably by estimation method but not by site.

DISCUSSION

Our results suggest that wastewater measurements alone might not provide sufficient data to reliably track disease trends in real-time. Instead, wastewater viral loads could be combined with additional public health surveillance data to improve predictions of other outcomes.

Wastewater surveillance for viral pathogens: A tool for public health

A focus on water quality has intensified globally, considering its critical role in sustaining life and ecosystems. Wastewater, reflecting societal development, profoundly impacts public health. Wastewater-based epidemiology (WBE) has emerged as a surveillance tool for detecting outbreaks early, monitoring infectious disease trends, and providing real-time insights, particularly in vulnerable communities. WBE aids in tracking pathogens, including viruses, in sewage, offering a comprehensive understanding of community health and lifestyle habits. With the rise in global COVID-19 cases, WBE has gained prominence, aiding in monitoring SARS-CoV-2 levels worldwide. Despite advancements in water treatment, poorly treated wastewater discharge remains a threat, amplifying the spread of water-, sanitation-, and hygiene (WaSH)-related diseases. WBE, serving as complementary surveillance, is pivotal for monitoring community-level viral infections. However, there is untapped potential for WBE to expand its role in public health surveillance. This review emphasizes the importance of WBE in understanding the link between viral surveillance in wastewater and public health, highlighting the need for its further integration into public health management.

Cross-continental comparative experiences of wastewater surveillance and a vision for the 21st century

The COVID-19 pandemic has brought the epidemiological value of monitoring wastewater into sharp focus. The challenges of implementing and optimising wastewater monitoring vary significantly from one region to another, often due to the array of different wastewater systems around the globe, as well as the availability of resources to undertake the required analyses (e.g. laboratory infrastructure and expertise). Here we reflect on the local and shared challenges of implementing a SARS-CoV-2 monitoring programme in two geographically and socio-economically distinct regions, São Paulo state (Brazil) and Wales (UK), focusing on design, laboratory methods and data analysis, and identifying potential guiding principles for wastewater surveillance fit for the 21st century. Our results highlight the historical nature of region-specific challenges to the implementation of wastewater surveillance, including previous experience of using wastewater surveillance, stakeholders involved, and nature of wastewater infrastructure. Building on those challenges, we then highlight what an ideal programme would look like if restrictions such as resource were not a constraint. Finally, we demonstrate the value of bringing multidisciplinary skills and international networks together for effective wastewater surveillance.

Wastewater-based Epidemiology for COVID-19 Surveillance: A Survey

The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology (WBE) for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding WBE for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.

Applying a Digital Twin and wastewater analysis for robust validation of COVID-19 pandemic forecasts: insights from Catalonia

Monitoring SARS-CoV-2 spread is challenging due to asymptomatic infections, numerous variants, and population behavior changes from non-pharmaceutical interventions. We developed a Digital Twin model to simulate SARS-CoV-2 evolution in Catalonia. Continuous validation ensures our model's accuracy. Our system uses Catalonia Health Service data to quantify cases, hospitalizations, and healthcare impact. These data may be under-reported due to screening policy changes. To improve our model's reliability, we incorporate data from the Catalan Surveillance Network of SARS-CoV-2 in Sewage (SARSAIGUA). This paper shows how we use sewage data in the Digital Twin validation process to identify discrepancies between model predictions and real-time data. This continuous validation approach enables us to generate long-term forecasts, gain insights into SARS-CoV-2 spread, reassess assumptions, and enhance our understanding of the pandemic's behavior in Catalonia.

Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models

The evident shedding of the SARS-CoV-2 RNA particles from infected individuals into the wastewater opened up a tantalizing array of possibilities for prediction of COVID-19 prevalence prior to symptomatic case identification through community testing. Many countries have therefore explored the use of wastewater metrics as a surveillance tool, replacing traditional direct measurement of prevalence with cost-effective approaches based on SARS-CoV-2 RNA concentrations in wastewater samples. Two important aspects in building prediction models are: time over which the prediction occurs and space for which the predicted case numbers is shown. In this review, our main focus was on finding mathematical models which take into the account both the time-varying and spatial nature of wastewater-based metrics into account. We used six main characteristics as our assessment criteria: i) modelling approach; ii) temporal coverage; iii) spatial coverage; iv) sample size; v) wastewater sampling method; and vi) covariates included in the modelling. The majority of studies in the early phases of the pandemic recognized the temporal association of SARS-CoV-2 RNA concentration level in wastewater with the number of COVID-19 cases, ignoring their spatial context. We examined 15 studies up to April 2023, focusing on models considering both temporal and spatial aspects of wastewater metrics. Most early studies correlated temporal SARS-CoV-2 RNA levels with COVID-19 cases but overlooked spatial factors. Linear regression and SEIR models were commonly used (n = 10, 66.6 % of studies), along with machine learning (n = 1, 6.6 %) and Bayesian approaches (n = 1, 6.6 %) in some cases. Three studies employed spatio-temporal modelling approach (n = 3, 20.0 %). We conclude that the development, validation and calibration of further spatio-temporally explicit models should be done in parallel with the advancement of wastewater metrics before the potential of wastewater as a surveillance tool can be fully realised.

Recent progress on wastewater-based epidemiology for COVID-19 surveillance: A systematic review of analytical procedures and epidemiological modeling

On March 11, 2020, the World Health Organization declared the coronavirus disease 2019 (COVID-19), whose causative agent is the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), a pandemic. This virus is predominantly transmitted via respiratory droplets and shed via sputum, saliva, urine, and stool. Wastewater-based epidemiology (WBE) has been able to monitor the circulation of viral pathogens in the population. This tool demands both in-lab and computational work to be meaningful for, among other purposes, the prediction of outbreaks. In this context, we present a systematic review that organizes and discusses laboratory procedures for SARS-CoV-2 RNA quantification from a wastewater matrix, along with modeling techniques applied to the development of WBE for COVID-19 surveillance. The goal of this review is to present the current panorama of WBE operational aspects as well as to identify current challenges related to it. Our review was conducted in a reproducible manner by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews. We identified a lack of standardization in wastewater analytical procedures. Regardless, the reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach was the most reported technique employed to detect and quantify viral RNA in wastewater samples. As a more convenient sample matrix, we suggest the solid portion of wastewater to be considered in future investigations due to its higher viral load compared to the liquid fraction. Regarding the epidemiological modeling, the data-driven approach was consistently used for the prediction of variables associated with outbreaks. Future efforts should also be directed toward the development of rapid, more economical, portable, and accurate detection devices.

Surveillance of SARS-CoV-2 in sewage from buildings housing residents with different vulnerability levels

During the last three years, various restrictions have been set up to limit the transmission of the Coronavirus Disease (COVID-19). While these rules apply at a large scale (e.g., country-wide level) human-to-human transmission of the virus that causes COVID-19, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), occurs at a small scale. Different preventive policies and testing protocols were implemented in buildings where COVID-19 poses a threat (e.g., elderly residences) or constitutes a disruptive force (e.g., schools). In this study, we sampled sewage from different buildings (a school, a university campus, a university residence, and an elderly residence) that host residents of different levels of vulnerability. Our main goal was to assess the agreement between the SARS-CoV-2 concentration in wastewater and the policies applied in these buildings. All buildings were sampled using passive samplers while 24 h composite samples were also collected from the elderly residence. Results showed that passive samplers performed comparably well to composite samples while being cost-effective to keep track of COVID-19 prevalence. In the elderly residence, the comparison of sampling protocols (passive vs. active) combined with the strict clinical testing allowed us to compare the sensitivities of the two methods. Active sampling was more sensitive than passive sampling, as the former was able to detect a COVID-19 prevalence of 0.4 %, compared to a prevalence of 2.2 % for passive sampling. The number of COVID-19-positive individuals was tracked clinically in all the monitored buildings. More frequent detection of SARS-CoV-2 in wastewater was observed in residential buildings than in non-residential buildings using passive samplers. In all buildings, sewage surveillance can be used to complement COVID-19 clinical testing regimes, as the detection of SARS-CoV-2 in wastewater remained positive even when no COVID-19-positive individuals were reported. Passive sampling is useful for building managers to adapt their COVID-19 mitigation policies.

Sewershed surveillance as a tool for smart management of a pandemic in threshold countries. Case study: Tracking SARS-CoV-2 during COVID-19 pandemic in a major urban metropolis in northwestern Argentina

Wastewater-based epidemiology is an economical and effective tool for monitoring the COVID-19 pandemic. In this study we proposed sampling campaigns that addressed spatial-temporal trends within a metropolitan area. This is a local study of detection and quantification of SARS-CoV-2 in wastewater during the onset, rise, and decline of COVID-19 cases in Salta city (Argentina) over the course of a twenty-one-week period (13 Aug to 30 Dec) in 2020. Wastewater samples were gathered from 13 sewer manholes specific to each sewershed catchment, prior to convergence or mixing with other sewer lines, resulting in samples specific to individual catchments with defined areas. The 13 sewershed catchments selected comprise 118,832 connections to the network throughout the city, representing 84.7 % (534,747 individuals) of the total population. The number of COVID19-related exposure and symptoms cases in each area were registered using an application developed for smartphones by the provincial government. Geographical coordinates provided by the devices were recorded, and consequently, it was possible to geolocalise all app-cases and track them down to which of the 13 sampling catchments belonged. RNA fragments of SARS-CoV-2 were detected in every site since the beginning of the monitoring, anticipating viral circulation in the population. Over the course of the 21-week study, the concentrations of SARS-CoV-2 ranged between 1.77 × 104 and 4.35 × 107 genome copies/L. There was a correspondence with the highest viral load in wastewater and the peak number of cases reported by the app for each catchment. The associations were evaluated with correlation analysis. The viral loads of SARS-CoV-2 in wastewater were a feasible means to describe the trends of COVID-19 infections. Surveillance at sewershed scale, provided reliable and strategic information that could be used by local health stakeholders to manage the COVID-19 pandemic.

Detection of SARS-CoV-2 RNA in wastewater and comparison to COVID-19 cases in two sewersheds, North Carolina, USA

Wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be useful for monitoring population-wide coronavirus disease 2019 (COVID-19) infections, especially given asymptomatic infections and limitations in diagnostic testing. We aimed to detect SARS-CoV-2 RNA in wastewater and compare viral concentrations to COVID-19 case numbers in the respective counties and sewersheds. Influent 24-hour composite wastewater samples were collected from July to December 2020 from two municipal wastewater treatment plants serving different population sizes in Orange and Chatham Counties in North Carolina. After a concentration step via HA filtration, SARS-CoV-2 RNA was detected and quantified by reverse transcription droplet digital polymerase chain reaction (RT-ddPCR) and quantitative PCR (RT-qPCR), targeting the N1 and N2 nucleocapsid genes. SARS-CoV-2 RNA was detected by RT-ddPCR in 100 % (24/24) and 79 % (19/24) of influent wastewater samples from the larger and smaller plants, respectively. In comparison, viral RNA was detected by RT-qPCR in 41.7 % (10/24) and 8.3 % (2/24) of samples from the larger and smaller plants, respectively. Positivity rates and method agreement further increased for the RT-qPCR assay when samples with positive signals below the limit of detection were counted as positive. The wastewater data from the larger plant generally correlated (⍴ ~0.5, p < 0.05) with, and even anticipated, the trends in reported COVID-19 cases, with a notable spike in measured viral RNA preceding a spike in cases when students returned to a college campus in the Orange County sewershed. Correlations were generally higher when using estimates of sewershed-level case data rather than county-level data. This work supports use of wastewater surveillance for tracking COVID-19 disease trends, especially in identifying spikes in cases. Wastewater-based epidemiology can be a valuable resource for tracking disease trends, allocating resources, and evaluating policy in the fight against current and future pandemics.

Five-week warning of COVID-19 peaks prior to the Omicron surge in Detroit, Michigan using wastewater surveillance

Wastewater-based epidemiology (WBE) is useful in predicting temporal fluctuations of COVID-19 incidence in communities and providing early warnings of pending outbreaks. To investigate the relationship between SARS-CoV-2 concentrations in wastewater and COVID-19 incidence in communities, a 12-month study between September 1, 2020, and August 31, 2021, prior to the Omicron surge, was conducted. 407 untreated wastewater samples were collected from the Great Lakes Water Authority (GLWA) in southeastern Michigan. N1 and N2 genes of SARS-CoV-2 were quantified using RT-ddPCR. Daily confirmed COVID-19 cases for the City of Detroit, and Wayne, Macomb, Oakland counties between September 1, 2020, and October 4, 2021, were collected from a public data source. The total concentrations of N1 and N2 genes ranged from 714.85 to 7145.98 gc/L and 820.47 to 6219.05 gc/L, respectively, which were strongly correlated with the 7-day moving average of total daily COVID-19 cases in the associated areas, after 5 weeks of the viral measurement. The results indicate a potential 5-week lag time of wastewater surveillance preceding COVID-19 incidence for the Detroit metropolitan area. Four statistical models were established to analyze the relationship between SARS-CoV-2 concentrations in wastewater and COVID-19 incidence in the study areas. Under a 5-week lag time scenario with both N1 and N2 genes, the autoregression model with seasonal patterns and vector autoregression model were more effective in predicting COVID-19 cases during the study period. To investigate the impact of flow parameters on the correlation, the original N1 and N2 gene concentrations were normalized by wastewater flow parameters. The statistical results indicated the optimum models were consistent for both normalized and non-normalized data. In addition, we discussed parameters that explain the observed lag time. Furthermore, we evaluated the impact of the omicron surge that followed, and the impact of different sampling methods on the estimation of lag time.