Computational analysis of SARS-CoV-2/COVID-19 surveillance by wastewater-based epidemiology locally and globally: Feasibility, economy, opportunities and challenges

Optimized methods for wastewater SARS-CoV-2 surveillance at military installations: direct extraction showed higher recoveries relative to polyethylene glycol precipitation

Wastewater surveillance is an economical approach to track community level pathogen prevalence and outbreak monitoring. Monitoring pathogens of public health concern in military installation wastewater can help mitigation efforts to protect service members. We used laboratory-prepared samples seeded with heat-inactivated SARS-CoV-2 and optimized two methods for wastewater SARS-CoV-2 surveillance. The first one is based on polyethylene glycol/sodium chloride precipitation to which fetal bovine serum (FBS) was added during precipitation (FBS-PEG/NaCl) followed by nucleic acid extraction and SARS-CoV-2 N1 and N2 specific RT-qPCR. This FBS-PEG/NaCl method (method-1) produced a visible pellet and improved recoveries (∼ 84.13 ± 9.12 % for N1 and 71.59 ± 9.77 % for N2, with 300 μL of added FBS) relative to standard PEG/NaCl method (no FBS; ∼7.98 ± 8.24 % for N1 and ∼ 7.42 ± 5.21 % for N2). The second one is a magnetic bead-based improved direct extraction method (method-2) wherein we showed that addition of carrier RNA (polyadenylic acid or poly-A) during extraction improved viral RNA recovery relative to direct extraction without poly-A. We evaluated the analytical sensitivity (process limit of detection; PLOD) and process efficiency of method-1 and method-2. The PLOD was 2,500 genome copies (GC)/L for method-1 and 12,500 GC/L for method-2. Controlled samples with serial dilutions of virus showed that method-1 is as effective in recovering the virus as method-2 at all concentrations of the virus tested. SARS-CoV-2 temporal trends were analyzed in 52 wastewater samples by method-2 of which the first 22 were also analyzed by method-1. SARS-CoV-2 trends correlate well in both approaches while the magnitude (copies/L) was much higher with method-2 (N1: 0.25-2.3 log; N2: 0.058-1.89 log) than with method-1. Possible reason for lower recoveries by method-1 with wastewater samples and the advantages of using controlled samples for method optimization are discussed. Better recoveries and higher throughput suggest improved direct extraction (method-2) is advantageous for wastewater SARS-CoV-2 surveillance.

Post-recovery viral shedding shapes wastewater-based epidemiological inferences

BACKGROUND

The prolonged viral shedding from the gastrointestinal tract is well documented for numerous pathogens, including SARS-CoV-2. However, the impact of prolonged viral shedding on epidemiological inferences using wastewater data is not yet fully understood.

METHODS

To gain a better understanding of this phenomenon at the population level, we extended a wastewater-based modeling framework that integrates viral shedding dynamics, viral load data in wastewater, case report data, and an epidemic model.

RESULTS

Our results indicate that as an outbreak progresses, the viral load from recovered individuals gradually becomes predominant, surpassing that from the infectious population. This phenomenon leads to a dynamic relationship between model-inferred and reported daily incidence over the course of an outbreak. Sensitivity analyses on the duration and rate of viral shedding for recovered individuals reveal that accounting for this phenomenon can considerably advance prediction of transmission peak timing. Furthermore, extensive viral shedding from the recovered population toward the conclusion of an epidemic wave may overshadow viral signals from newly infected cases carrying emerging variants, which can delay the rapid recognition of emerging variants based on viral load.

CONCLUSIONS

These findings highlight the necessity of integrating post-recovery viral shedding to enhance the accuracy and utility of wastewater-based epidemiological analysis.

Wastewater Surveillance to Estimate and Characterize Hepatitis E Virus Circulation

Hepatitis E virus (HEV) is a cause of enterically transmitted hepatitis around the world. Because of the high frequency of asymptomatic infections, the magnitude of HEV infection is underestimated. Wastewater monitoring could be useful to improve our knowledge on HEV epidemiology. In this study, we analyzed the capacity of wastewater surveillance to give an insight into the circulation and the diversity of HEV in two French cities. HEV RNA was detected and quantified by digital PCR in 115 untreated composite wastewater samples collected weekly at the inlet of wastewater treatment plants (WWTPs), 58 at Toulouse WWTP and 57 at Dunkerque WWTP. Plasma HEV RNA in blood donors was detected by a commercial assay (Roche Cobas) over the same period in the same area. HEV diversity was analyzed using long-read single-molecule real-time sequencing (Pacific Biosciences). HEV RNA was detected in 88% and 95% wastewater samples collected at Toulouse (Occitanie region, Southern France) and Dunkerque (Hauts-de-France region, Northern France) WWTPs, respectively. HEV RNA concentration ranged between 4.1 and 5.7 log copies/L and was almost similar between the two sites. A long orf2 fragment of HEV genome (1030 nucleotides) was obtained and sequenced in 45% and 70% of positive HEV RNA wastewater samples collected at Toulouse site and Dunkerque site, respectively. Out of 31 strains identified in Toulouse wastewater, 24 were HEV-3c (77%), 6 were HEV-3f (19%), and 1 was HEV-3h (3%). Out of 55 strains identified in Dunkerque, 30 were HEV-3c (55%) and 25 were HEV-3f (45%). All HEV RNA-positive samples from blood donors that could be genotyped during the study period contained HEV-3. Subtype distribution in 51 blood donors living in Toulouse did not differ from that in Toulouse wastewater. The HEV-3 subtype distribution in 51 Hauts-de-France region blood donors and in Dunkerque wastewater were different, but the predominant subtype was the same (HEV-3c). Lastly, we explored the link between the measurement of viral loads in wastewater and the extent of infection in the served population. Although a good correlation between the peaks of positive HEV RNA estimated in wastewater samples and that observed in blood donors was observed with a lag of + 3 weeks for Toulouse, the correlation was weaker for Dunkerque. Wastewater surveillance system applied locally could be very useful for assessing the HEV infection status of a population.

Advances in Wastewater-Based Epidemiology for Pandemic Surveillance: Methodological Frameworks and Future Perspectives

Wastewater-based epidemiology (WBE) has emerged as a transformative approach for community-level health monitoring, particularly during the COVID-19 pandemic. This review critically examines the methodological framework of WBE systems through the following three core components: (1) sampling strategies that address spatial-temporal variability in wastewater systems, (2) comparative performance of different platforms in pathogen detection, and (3) predictive modeling integrating machine learning approaches. We systematically analyze how these components collectively overcome the limitations of conventional surveillance methods through early outbreak detection, asymptomatic case identification, and population-level trend monitoring. While highlighting technical breakthroughs in viral concentration methods and variant tracking through sequencing, the review also identifies persistent challenges, including data standardization, cost-effectiveness concerns in resource-limited settings, and ethical considerations in public health surveillance. Drawing insights from global implementation cases, we propose recommendations for optimizing each operational phase and discuss emerging applications beyond pandemic response. This review highlights WBE as an indispensable tool for modern public health, whose methodological refinements and cross-disciplinary integration are critical for transforming pandemic surveillance from reactive containment to proactive population health management.

Subsewershed analyses of the impacts of inflow and infiltration on viral pathogens and antibiotic resistance markers across a rural sewer system

As wastewater-based surveillance (WBS) is increasingly used to track community-level disease trends, it is important to understand how pathogen signals can be altered by phenomena that occur within sewersheds such as inflow and infiltration (I&I). Our objectives were to characterize I&I across a rural sewershed and assess potential impacts on viral (rotavirus, norovirus GII, and SARS-CoV-2), fecal indicator (HF183, the hCYTB484 gene specific to the human mitochondrial genome, and crAssphage), and antimicrobial resistance (intI1, blaCTX-M-1) targets. In a small town in Virginia (USA), we collected 107 wastewater samples at monthly intervals over a 12-month period (2022-2023) at the wastewater treatment plant (WWTP) influent and 11 up-sewer sites. Viral, fecal indicator, and antimicrobial resistance targets were enumerated using ddPCR. Physicochemical proxies for organics and nutrient levels in sewage (chemical oxygen demand (COD), total suspended solids (TSS), and NH3(aq)) and genetic markers of anthropogenic impact were used to characterize I&I across the sewershed. Overall, precipitation was negatively associated (Spearman test; ρ < 0; p < 0.01) with physicochemical markers (TSS, COD, K, PO43--P, NH3(aq)) in the WWTP influent. We observed the highest concentrations of human fecal markers and a measure anthropogenic pollution and antibiotic resistance (intI1) in up-sewer sites with limited I&I. However, median viral gene copy concentrations were highest at the WWTP, compared to 100 % (n = 11), 90 % (n = 10), and 55 % (n = 6) of up-sewer sites for rotavirus, norovirus GII, and SARS-CoV-2, respectively. After adjusting for covariates (Ba, COD, dissolved oxygen, groundwater depth, precipitation, sampling date) using generalized linear models, moderate to high I&I was associated with statistically significant reductions in log10-transformed rotavirus and norovirus GII concentrations across the sewershed (coefficients = -0.7 and -0.9, p < 0.001, n = 95), though not for SARS-CoV-2 (coefficient = -0.2, p = 0.181, n = 95). Overall, we found that while I&I can diminish biomarker signals throughout a sewershed, including at the WWTP influent, I&I impacts vary depending on the target, and pathogen biomarker signals were, on average, higher and less variable over time at the WWTP compared to most up-sewer sites. As far as we are aware, this is the first study to assess in situ I&I impacts on multiple WBS targets. Taken together, our findings highlight challenges and tradeoffs associated with different sampling strategies for different WBS targets in heavily I&I impacted systems.

The potential of long-term wastewater-based surveillance to predict COVID-19 waves peak in Mexico

Wastewater-based surveillance (WBS) is valuable method for monitoring the dispersion of pathogens at a low cost. However, their impact on public health decision-making is limited because there is a lack of long-term analyses, especially in low- and middle-income countries. This study aimed to assess the effectiveness of using WBS to predict the occurrence of COVID-19 waves and estimate the prevalence of infection, emphasizing the impact of SARS-CoV-2 variants. During 17 months of influent monitoring of two wastewater treatment plants in Queretaro City, Mexico, wave prediction time was influenced by variant dispersion. Waves dominated by the Delta and Omicron variants circulation showed lead days values from 5 to 14 and 1 to 4 days, respectively. According to the Monte Carlo model, disease prevalence prediction by WBS aligned with clinically reported cases at wave onsets, but the variant's transmissibility explained the overestimation during peaks. This work provides new insights into the potential and limitations of using WBS as an epidemiological tool for detecting pathogens and predicting their occurrence. PRACTITIONER POINTS: Long-term wastewater monitoring allowed early prediction of COVID-19 case waves. The prediction capability is related to the variant presence and their infectivity. The prevalence estimated by wastewater surveillance was higher in all case waves. The prevalence estimation has limitations regarding variations in data input.

Wastewater Speaks: Evaluating SARS-CoV-2 Surveillance, Sampling Methods, and Seasonal Infection Trends on a University Campus

Wastewater surveillance has emerged as a cost-effective and equitable approach for tracking the spread of SARS-CoV-2. In this study, we monitored the prevalence of SARS-CoV-2 on a university campus over three years (2021-2023) using wastewater-based epidemiology (WBE). Wastewater samples were collected from 11 manholes on campus, each draining wastewater from a corresponding dormitory building, and viral RNA concentrations were measured using reverse transcription-quantitative PCR (RT-qPCR). Weekly clinical case data were also obtained from the university health center. A strong positive and significant correlation was observed between Grab and Composite sampling methods, supporting their robustness as equally effective approaches for sample collection. Specifically, a strong correlation was observed between Aggie Village 4 Grab and Aggie Village 4 Composite samples (R2 = 0.84, p = 0.00) and between Barbee Grab and Barbee Composite samples (R2 = 0.80, p = 0.00). Additionally, higher viral RNA copies of SARS-CoV-2 (N1 gene) were detected during the Spring semester compared to the Fall and Summer semesters. Notably, elevations in raw N1 concentrations were observed shortly after the return of college students to campus, suggesting that these increases were predominantly associated with students returning at the beginning of the Fall and Spring semesters (January and August). To account for variations in fecal loading, SARS-CoV-2 RNA concentrations were normalized using Pepper Mild Mottle Virus (PMMoV), a widely used viral fecal biomarker. However, normalization using PMMoV did not improve correlations between SARS-CoV-2 RNA levels and clinical case data. Despite these findings, our study did not establish WBE as a consistently reliable complement to clinical testing in a university campus setting, contrary to many retrospective studies. One key limitation was that numerous off-campus students did not contribute to the campus wastewater system corresponding to the monitored dormitories. However, some off-campus students were still subjected to clinical testing at the university health center under mandated protocols. Moreover, the university health center discontinued reporting cases per dormitory after 2021, making direct comparisons more challenging. Nevertheless, this study highlights the continued value of WBE as a surveillance tool for monitoring infectious diseases and provides critical insights into its application in campus environments.

Seasonality of enteric viruses and correlation of hepatitis a virus in wastewater with clinical cases

Human adenovirus F41 (HAdV-41), norovirus genogroup II (HNV-GII), rotavirus group A (RVA), and hepatitis A virus (HAV) are responsible for millions of illnesses every year in Canada. Wastewater-based epidemiology is one way to monitor the prevalence of these underreported (HAV) and non-reportable (HAdV-41, HNV-GII, RVA) food and waterborne enteric viruses. In this study, we monitored the presence of these four viruses in wastewater over 16 months from September 2022 until December 2023 using samples from two locations in southern Ontario. Viruses in 286 wastewater samples were concentrated using PEG precipitation and quantified using a multiplex RT-qPCR assay for HAdV-41, HNV-GII, and RVA, and a singleplex RT-qPCR assay for HAV. In agreement with historical clinical data, HNV-GII and RVA had seasonal peaks in wastewater in the winter (HNV-GII, up to 1.09 × 103 gene copies (GC)/mL) and spring (RVA, up to 1.20 × 102 GC/mL). The concentration of HAdV-41 in wastewater had a significant seasonal peak in the fall of 2022 (up to 4.65 × 104 GC/mL) that was not repeated in the fall of 2023. The detection of HAV in 24 of 127 samples was correlated with four clinical cases in one sewershed with a one-week wastewater lead time.

Wastewater surveillance of open drains for mapping the trajectory and succession of SARS-CoV-2 lineages in 23 cities of Maharashtra state (India) during June 2022 to May 2023

The timely detection of SARS-CoV-2 is crucial for controlling its spread, especially in areas vulnerable to outbreaks. However, due to a lack of sustainable and low cost methods, early detection of such outbreaks is impacting low to middle-income countries (LMICs). Leveraging Wastewater-Based Epidemiology (WBE), we examined the dissemination and evolution of the SARS CoV2 virus in open drains across urban, suburban and densely populated cities in selected regions in the state of Maharashtra, the third largest state of India. In the period from June 2022 to May 2023, 44.89 % of SARS-CoV-2 RNA were positive in RT-qPCR in wastewater samples collected from open drains across selected regions. Whole genome sequencing revealed 22 distinct SARS-CoV-2 lineages, with the Omicron variant, followed by the XBB variant, dominating, alongside other variants such as BF, BQ, CH, and BA.2.86, albeit with lower frequencies. Wastewater surveillance provided early insights into viral transmission, complementing clinical surveillance. Notably, our study detected emerging variants prior to clinical reporting, highlighting the potential of WBE for early detection. Findings underscore the correlation between population density and the trend of viral load. This study also highlighted the significance of using open drains for WBE as a low-cost, and sustainable tool, especially in LMICs, where adequate methods are lacking or difficult to deploy for accessibility.

Ethical Considerations for Wastewater Surveillance Conducted by the US Department of Defense

Unlabelled

The US Department of Defense (DoD) is establishing its wastewater surveillance capacities to support national security objectives and promote the public health and medical readiness of US service members. Wastewater surveillance is an emerging technology that has traditionally been leveraged for detecting infectious diseases. However, its potential future applications could yield a vast and unpredictable amount of information that could be used for a wide variety of both health- and nonhealth-related purposes. The US military also serves an inimitable role for the country and its citizens, and exercises significant levels of control over its service members compared to civilian organizations. Further, its present and potential wastewater surveillance activities may reach far beyond just military installations. These factors raise unique ethical considerations that must be accounted for by leaders and policymakers to ensure the DoD implements a wastewater surveillance network in a manner that is both impactful in supporting public health and appropriate to the scope and population under surveillance. This paper explores important ethical features in conducting wastewater surveillance that are both specific to the DoD experience and applicable to wider public health initiatives.

The influence of environmental factors on the detection and quantification of SARS-CoV-2 variants in dormitory wastewater at a primarily undergraduate institution

Testing for the causative agent of coronavirus disease 2019 (COVID-19), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been crucial in tracking disease spread and informing public health decisions. Wastewater-based epidemiology has helped to alleviate some of the strain of testing through broader, population-level surveillance, and has been applied widely on college campuses. However, questions remain about the impact of various sampling methods, target types, environmental factors, and infrastructure variables on SARS-CoV-2 detection. Here, we present a data set of over 800 wastewater samples that sheds light on the influence of a variety of these factors on SARS-CoV-2 quantification using droplet digital PCR (ddPCR) from building-specific sewage infrastructure. We consistently quantified a significantly higher number of copies of virus per liter for the target nucleocapsid 2 (N2) compared to nucleocapsid 1 (N1), regardless of the sampling method (grab vs composite). We further show some dormitory-specific differences in SARS-CoV-2 abundance, including correlations to dormitory population size. Environmental variables like precipitation and temperature show little to no impact on virus load, with the exception of higher temperatures for grab sample data. We observed significantly higher gene copy numbers of the Omicron variant than the Delta variant within ductile iron pipes but no difference in nucleocapsid abundance (N1 or N2) across the three different sewage pipe types in our data set. Our results indicate that contextual variables should be considered when interpreting wastewater-based epidemiological data.

IMPORTANCE

Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has been crucial in tracking the spread of the virus and informing public health decisions. SARS-CoV-2 viral RNA is shed by symptomatic and asymptomatic infected individuals, allowing its genetic material to be detected and quantified in wastewater. Here, we used wastewater-based epidemiology to measure SARS-CoV-2 viral RNA from several dormitories on the Appalachian State University campus and examined the impact of sampling methods, target types, environmental factors, and infrastructure variables on quantification. Changes in the quantification of SARS-CoV-2 were observed based on target type, as well as trends for the quantification of the Delta and Omicron variants by sampling method. These results highlight the value of applying the data-inquiry practices used in this study to better contextualize wastewater sampling results.

Culture-Dependent and -Independent Wastewater Surveillance for Multiple Pathogenic Yeasts

Wastewater surveillance is a promising tool to monitor potential outbreaks and determine the disease burden within a community. This system has been extensively used to monitor polio and COVID-19 infection levels, yet few attempts have been made to apply it to monitoring pathogenic yeast. This study aimed to investigate the application of wastewater surveillance for potentially pathogenic yeast in wastewater treatment plant influent. This was done by comparing culture-dependent data with culture-independent data and investigating the fluconazole concentration in wastewater. Additional studies on the growth of isolated strains were conducted. We found that a multiplex PCR system to detect multiple yeasts holds promise as a molecular detection tool for wastewater surveillance. Culture-dependent results indicated that Candida spp. specifically C. krusei and C. glabrata, were most prominent. Growth studies supported that these species grow well in this environment while the less frequently isolated yeasts grew poorly. The data from culture-dependent and independent techniques showed some correlation, with similar species being identified with both, further promoting the use of molecular tools for surveillance. This study highlights the presence of potentially pathogenic yeasts in wastewater, which may indicate the prevalence of these yeasts in the environment or community. This wastewater may also be a potential source of infection for persons encountering it due to poor wastewater management.

Temporal, spatial, and methodological considerations in evaluating the viability of measles wastewater surveillance

Measles is a highly transmissible disease of increasing concern due to waning vaccination contributing to a significant rise in measles cases, with 283 reported cases and 16 outbreaks in the U.S. as of November 7, 2024. Early identification of measles cases is thus critical to disease containment and control. Wastewater-based epidemiology (WBE) represents a potential strategy for the efficient identification of measles outbreaks. We investigated the suitability of WBE for measles outbreak identification by using a model-based approach to elucidate the relationship between measles shedding, wastewater concentration, and detectability. The model reveals conditions for effective detection, specifying the optimal timing, location, and methodology needed to achieve a specific probability of detection, including accounting for geographic variability of wastewater generation and measles case rates. Measles RNA shedding, primarily from urine, contributes an average of 8.72 log10 genome copies (GC) daily per infection into sewage. At the average U.S. wastewater treatment plant (WWTP), achieving a 50 % probability of detection requires approximately 78 cases per 100,000 people with a process limit of detection (PLOD) of 3.0 log10 GC/L. At a PLOD of 3.0 log10 GC/L, over half of all WWTPs in the world can detect a single hypothetical measles case at a 10 % probability of detection. However, achieving a 50-90 % detection rate is challenging, especially with a higher PLOD, except in areas with the highest measles cases. Some locations require case levels consistent with a complete lack of vaccination for feasible measles detection in wastewater. Future work exploring measles shedding, variable shedding behavior, and local case rates can enhance model predictions. Overall, this analysis suggests that WBE detection of measles in most locations remains challenging without a significant increase in case rates or technical improvements decreasing the PLOD.

Wastewater-based epidemiology: deriving a SARS-CoV-2 data validation method to assess data quality and to improve trend recognition

Introduction

Accurate and consistent data play a critical role in enabling health officials to make informed decisions regarding emerging trends in SARS-CoV-2 infections. Alongside traditional indicators such as the 7-day-incidence rate, wastewater-based epidemiology can provide valuable insights into SARS-CoV-2 concentration changes. However, the wastewater compositions and wastewater systems are rather complex. Multiple effects such as precipitation events or industrial discharges might affect the quantification of SARS-CoV-2 concentrations. Hence, analysing data from more than 150 wastewater treatment plants (WWTP) in Germany necessitates an automated and reliable method to evaluate data validity, identify potential extreme events, and, if possible, improve overall data quality.

Methods

We developed a method that first categorises the data quality of WWTPs and corresponding laboratories based on the number of outliers in the reproduction rate as well as the number of implausible inflection points within the SARS-CoV-2 time series. Subsequently, we scrutinised statistical outliers in several standard quality control parameters (QCP) that are routinely collected during the analysis process such as the flow rate, the electrical conductivity, or surrogate viruses like the pepper mild mottle virus. Furthermore, we investigated outliers in the ratio of the analysed gene segments that might indicate laboratory errors. To evaluate the success of our method, we measure the degree of accordance between identified QCP outliers and outliers in the SARS-CoV-2 concentration curves.

Results and discussion

Our analysis reveals that the flow and gene segment ratios are typically best at identifying outliers in the SARS-CoV-2 concentration curve albeit variations across WWTPs and laboratories. The exclusion of datapoints based on QCP plausibility checks predominantly improves data quality. Our derived data quality categories are in good accordance with visual assessments.

Conclusion

Good data quality is crucial for trend recognition, both on the WWTP level and when aggregating data from several WWTPs to regional or national trends. Our model can help to improve data quality in the context of health-related monitoring and can be optimised for each individual WWTP to account for the large diversity among WWTPs.

Tracking SARS-CoV-2 variants in wastewater in San Pedro de la Paz, Chile

Studies have shown the presence of SARS-CoV-2 in the stool of both symptomatic and asymptomatic COVID-19 patients, enabling wastewater-based surveillance (WBS) to complement clinical monitoring. The emergence of variants can enhance viral transmissibility, highlighting the need for ongoing surveillance to detect and control infectious diseases. This study aimed to detect SARS-CoV-2 variants in wastewater from a treatment plant in San Pedro de la Paz, Chile, between January and November 2021. Wastewater samples were concentrated using the polyethylene glycol method, and RT-qPCR assays were performed to analyze SARS-CoV-2 and its variants (Alpha, Beta, Gamma, Lambda, and Delta), with results compared to Illumina amplicon sequencing. The concentration method achieved about 11% viral recovery. The detection of viruses and variants in wastewater proved sensitive and consistent with clinical data, providing additional surveillance insights. Notably, Lambda and Delta variants were the most frequently detected during the second and third infection waves, with some variants identified in wastewater before the first confirmed clinical cases. However, Illumina sequencing lacked sufficient genome coverage, suggesting the need for better sequencing methods for this matrix. This study demonstrates that WBS is a rapid, cost-effective tool for detecting SARS-CoV-2 and its mutations, particularly useful during overwhelming clinical situations or when cost is prohibitively high.

Screening potential biomarkers for acute respiratory infectious diseases from antipyretics, antiviral, and antibiotics in wastewater

Since the onset of COVID-19, respiratory diseases have emerged as a focal concern within the field of public health. This study aims to reveal the prevalence of acute respiratory infectious diseases by screening antipyretic, antiviral, and antibiotic biomarkers through wastewater analysis. Samples were collected over a seven-day period each year in 2022, 2023, and 2024 from a northern city in China, assessing the concentrations of two antipyretics (paracetamol and ibuprofen), one antiviral drug (oseltamivir), eleven antibiotics, and three pathogens (influenza A, influenza B, and Mycoplasma pneumoniae). The usage of most antipyretics and antibiotics was higher in 2023 and 2024, primarily due to the outbreak of COVID-19 in 2023 and the prevalence of influenza A, influenza B, and Mycoplasma pneumoniae in 2024. The prevalence assessed using antipyretics (2.68 %) and pathogens (2.70 %) demonstrated a high degree of consistency, whereas the prevalence estimated using antibiotics and antiviral drugs was only 0.53 % and 0.36 %, respectively. Antibiotics are generally used to treat a broad spectrum of bacterial infections rather than targeting a specific pathogen, so their presence in wastewater may not directly reflect the prevalence of a particular disease. In contrast, antipyretics and specific pathogens exhibit a stronger correlation, suggesting that they may serve as more reliable biomarkers than antiviral and antibiotic drugs. The research findings offer alternative biomarkers, such as antipyretics, aside from pathogens, for the assessment of acute respiratory infectious diseases.

Evaluating survey techniques in wastewater-based epidemiology for accurate COVID-19 incidence estimation

Wastewater-based epidemiology (WBE) requires high-quality survey methods to determine the incidence of infections in wastewater catchment areas. In this study, the wastewater survey methods necessary for comprehending the incidence of infection by WBE are clarified. This clarification is based on the correlation with the number of confirmed coronavirus disease 2019 (COVID-19) cases, considering factors such as handling non-detect data, calculation method for representative values, analytical sensitivity, analytical reproducibility, sampling frequency, and survey duration. Data collected from 15 samples per week for two and a half years using a highly accurate analysis method were regarded as gold standard data, and the correlation between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater and confirmed COVID-19 cases was analyzed by Monte Carlo simulation under the hypothetical situation where the quality of the wastewater survey method was reduced. Regarding data handling, it was appropriate to replace non-detect data with estimates based on distribution, and to use geometric means to calculate representative values. For the analysis of SARS-CoV-2 RNA in samples, using a highly sensitive and reproducible method (non-detect rates of <40 %; ≤0.4 standard deviation) and surveying at least three samples, preferably five samples, per week were considered desirable. Furthermore, conducting the survey over a period of time that included at least 50 weeks was necessary. A WBE that meets these survey criteria is sufficient for the determination of the COVID-19 infection incidence in the catchment. Furthermore, WBE can offer additional insights into infection rates in the catchment, such as the estimated 48 % decrease in confirmed COVID-19 cases visiting a clinic following a COVID-19 legal reclassification in Japan.

Optimizing spatial distribution of wastewater-based epidemiology to advance health equity

In 2022, the US Centers for Disease Control and Prevention commissioned the National Academies of Sciences, Engineering, and Medicine to assess the role of community-level wastewater-based epidemiology (WBE) beyond COVID-19. WBE is recognized as a promising mechanism for promptly identifying infectious diseases, including COVID-19 and other novel pathogens. An important conclusion from this initiative is the critical importance of maintaining equity and expanding access to fully realize the benefits of wastewater surveillance for marginalized communities. To address this need, we propose an optimization framework that strategically allocates wastewater monitoring resources at the wastewater treatment plant (WWTP) level, ensuring more effective and equitable distribution of surveillance efforts to serve underserved populations. The purpose of the framework is to obtain a balanced spatial distribution, inclusive population coverage, and efficient representation of disadvantaged groups in the allocation of resources for WBE. Furthermore, the framework concentrates on areas with high population density and gives priority to vulnerable regions, as well as identifying signals that display significant variations from other monitored sources. The optimization objective is to maximize a weighted combination of these critical factors. This problem is formulated as an integer optimization problem and solved using simulated annealing. We evaluate various scenarios, considering different weighting factors, to optimize the allocation of WWTPs with monitoring systems. This optimization framework provides an opportunity to enhance WBE by providing customized monitoring strategies created to address specific priorities and situations, thus enhancing the decision-making processes in public health responses.

Definition of a concentration and RNA extraction protocol for optimal whole genome sequencing of SARS-CoV-2 in wastewater (ANRS0160)

Monitoring the presence of RNA from emerging pathogenic viruses, such as SARS-CoV-2, in wastewater (WW) samples requires suitable methods to ensure an effective response. Genome sequencing of WW is one of the crucial methods, but it requires high-quality RNA in sufficient quantities, especially for monitoring emerging variants. Consequently, methods for viral concentration and RNA extraction from WW samples have to be optimized before sequencing. The purpose of this study was to achieve high coverage (≥ 90 %) and sequencing depth (at least ≥200×) even for low initial RNA concentrations (< 105 genome copies (GC)/L) in WW. A further objective was to determine the range of SARS-CoV-2 RNA concentrations that allow high-quality sequencing, and the optimal sample volume for analysis. Ultrafiltration (UF) methods were used to concentrate viral particles from large influent samples (up to 500 mL). An RNA extraction protocol using silica beads, neutral phenol-chloroform treatment, and a PCR inhibitor removal kit was chosen for its effectiveness in extracting RNA and eliminating PCR inhibitors, as well as its adaptability for use with large influent samples. Recovery rates ranged from 24 % to 63 % (N = 17) for SARS-CoV-2 naturally present in WW samples. 200 mL WW samples can be enough for UF concentration, as they showed high quality sequencing analyses with between 5 × 104 GC/L and 6 × 103 GC/L. Below 6 × 103 GC/L, high-quality sequencing was also achieved for ∼40 % of the samples using 500 mL of WW. Sequencing analysis for variant detection was performed on 200 mL WW samples with coverage of >95 % and sequencing depth of >1000×. Analyses revealed the predominance of variant EG.5, known as Eris (66 %-100 %). The use of UF methods in combination with a suitable RNA extraction protocol appear promising for sequencing enveloped viruses in WW in a context of viral emergence.

Detection of SARS-CoV-2 in wastewater as an earlier predictor of COVID-19 epidemic peaks in Venezuela

Wastewater-based epidemiological surveillance has proven to be a useful and cost-effective tool for detecting COVID-19 outbreaks. Here, our objective was to evaluate its potential as an early warning system in Venezuela by detecting SARS-CoV-2 RNA in wastewater and its correlation with reported cases of COVID-19. Viral RNA was concentrated from wastewater collected at various sites in Caracas (northern Venezuela), from September 2021 to July 2023, using the polyethylene glycol (PEG) precipitation method. Viral quantification was performed by RT-qPCR targeting the N1 and ORF1ab genes. A significant association (p < 0.05) was found between viral load in wastewater and reported cases of COVID-19 up to six days after sampling. During the whole study, two populated areas of the city were persistent hotspots of viral infection. The L452R mutation, suggestive of the presence of the Delta variant, was identified in the only sample where a complete genomic sequence could be obtained. Significant differences (p < 0.05) between the physicochemical conditions of the wastewater samples positive and negative for the virus were found. Our results support proof of concept that wastewater surveillance can serve as an early warning system for SARS-CoV-2 outbreaks, complementing public health surveillance in those regions where COVID-19 is currently underreported.

A mechanistic modeling and estimation framework for environmental pathogen surveillance

Environmental pathogen surveillance is a promising disease surveillance modality that has been widely adopted for SARS-CoV-2 monitoring. The highly variable nature of environmental pathogen data is a challenge for integrating these data into public health response. One source of this variability is heterogeneous infection both within an individual over the course of infection as well as between individuals in their pathogen shedding over time. We present a mechanistic modeling and estimation framework for connecting environmental pathogen data to the number of infected individuals. Infected individuals are modeled as shedding pathogen into the environment via a Poisson process whose rate parameter λt varies over the course of their infection. These shedding curves λt are themselves random, allowing for variation between individuals. We show that this results in a Poisson process for environmental pathogen levels with rate parameter a function of the number of infected individuals, total shedding over the course of infection, and pathogen removal from the environment. Theoretical results include determination of identifiable parameters for the model from environmental pathogen data and simple, explicit formulas for the likelihood for particular choices of individual shedding curves. We give a two step Bayesian inference framework, where the first step corresponds to calibration from data where the number of infected individuals is known, followed by an estimation step from environmental surveillance data when the number of infected individuals is unknown. We apply this modeling and estimation framework to synthetic data, as well as to an empirical case study of SARS-CoV-2 in environmental dust collected from isolation rooms housing university students. Both the synthetic data and empirical case study indicate high inter-individual variation in shedding, leading to wide credible intervals for the number of infected individuals. We examine how uncertainty in estimates of the number of infected individuals from environmental pathogen levels scales with the true number of infected individuals and model misspecification. While credible intervals for the number of infected individuals are wide, our results suggest that distinguishing between no infection and small-to-moderate levels of infection (≈10 infected individuals) may be possible, and that it is broadly possible to differentiate between moderate (≈40) and high (≈200) numbers of infected individuals.

A Narrative Review of High Throughput Wastewater Sample Processing for Infectious Disease Surveillance: Challenges, Progress, and Future Opportunities

During the recent COVID-19 pandemic, wastewater-based epidemiological (WBE) surveillance played a crucial role in evaluating infection rates, analyzing variants, and identifying hot spots in a community. This expanded the possibilities for using wastewater to monitor the prevalence of infectious diseases. The full potential of WBE remains hindered by several factors, such as a lack of information on the survival of pathogens in sewage, heterogenicity of wastewater matrices, inconsistent sampling practices, lack of standard test methods, and variable sensitivity of analytical techniques. In this study, we review the aforementioned challenges, cost implications, process automation, and prospects of WBE for full-fledged wastewater-based community health screening. A comprehensive literature survey was conducted using relevant keywords, and peer reviewed articles pertinent to our research focus were selected for this review with the aim of serving as a reference for research related to wastewater monitoring for early epidemic detection.

Equity in wastewater monitoring: Differences in the demographics and social vulnerability of sewered and unsewered populations across North Carolina

Wastewater monitoring is a valuable public health tool that can track a variety of health markers. The strong correlations between trends in wastewater viral concentrations and county-level COVID-19 case counts point to the ability of wastewater data to represent changes in a community's disease burden. However, studies are lacking on whether the populations sampled through wastewater monitoring represent the characteristics of the broader community and the implications on health equity. We conducted a geospatial analysis to examine the extent to which populations contributing to wastewater collected through the North Carolina Wastewater Monitoring Network as of June 2022 represent the broader countywide and statewide populations. After intersecting sewershed boundary polygons for 38 wastewater treatment plants across 18 counties with census block and tract polygons, we compared the demographics and social vulnerability of (1) people residing in monitored sewersheds with countywide and statewide populations, and (2) sewered residents, regardless of inclusion in wastewater monitoring, with unsewered residents. We flagged as meaningful any differences greater than +/- 5 percentage points or 5 percent (for categorical and continuous variables, respectively) and noted statistically significant differences (p < 0.05). We found that residents within monitored sewersheds largely resembled the broader community on most variables analyzed, with only a few exceptions. We also observed that when multiple sewersheds were monitored within a county, their combined service populations resembled the county population, although individual sewershed and county populations sometimes differed. When we contrasted sewered and unsewered populations within a given county, we found that sewered populations were more vulnerable than unsewered populations, suggesting that wastewater monitoring may fill in the data gaps needed to improve health equity. The approach we present here can be used to characterize sewershed populations nationwide to ensure that wastewater monitoring is implemented in a manner that informs equitable public health decision-making.

The comparison of decay rates of infectious SARS-CoV-2 and viral RNA in environmental waters and wastewater

Understanding the decay characteristics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater and ambient waters is important for multiple applications including assessment of risk of exposure associated with handling wastewater samples, public health risk associated with recreation in wastewater polluted ambient waters and better understanding and interpretation of wastewater-based epidemiology (WBE) results. We evaluated the decay rates of infectious SARS-CoV-2 and viral RNA in wastewater and ambient waters under temperature regimes representative of seasonal fluctuations. Infectious virus was seeded in autoclaved primary wastewater effluent, final dechlorinated wastewater effluent, lake water, and marine water at a final concentration of 6.26 ± 0.07 log10 plaque forming units per milliliter. Each suspension was incubated at either 4°, 25°, and 37 °C. Samples were initially collected on an hourly basis, then approximately every other day for 15 days. All samples were analyzed for infectious virus via a plaque assay using the Vero E6 cell line, and viral gene copy levels were quantified with the US CDC's N1 and N2 reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) assays. The infectious virus decayed significantly faster (p ≤ 0.0214) compared to viral RNA, which persisted for the duration of the study irrespective of the incubation conditions. The initial loss (within 15 min of seeding) as well as decay of infectious SARS-CoV-2 was significantly faster (p ≤ 0.0387) in primary treated wastewater compared to other water types, but viral RNA did not degrade appreciably in this matrix until day 15. Overall, temperature was the most important driver of decay, and after 24 h, no infectious SARS-CoV-2 was detected at 37 °C in any water type. Moreover, the CDC N2 gene assay target decayed significantly (p ≤ 0.0174) faster at elevated temperatures compared to CDC N1, which has important implications for RT-qPCR assay selection for WBE approach.

Comparative analyses of SARS-CoV-2 RNA concentrations in Detroit wastewater quantified with CDC N1, N2, and SC2 assays reveal optimal target for predicting COVID-19 cases

To monitor COVID-19 through wastewater surveillance, global researchers dedicated significant endeavors and resources to develop and implement diverse RT-qPCR or RT-ddPCR assays targeting different genes of SARS-CoV-2. Effective wastewater surveillance hinges on the appropriate selection of the most suitable assay, especially for resource-constrained regions where scant technical and socioeconomic resources restrict the options for testing with multiple assays. Further research is imperative to evaluate the existing assays through comprehensive comparative analyses. Such analyses are crucial for health agencies and wastewater surveillance practitioners in the selection of appropriate methods for monitoring COVID-19. In this study, untreated wastewater samples were collected weekly from the Detroit wastewater treatment plant, Michigan, USA, between January and December 2023. Polyethylene glycol precipitation (PEG) was applied to concentrate the samples followed by RNA extraction and RT-ddPCR. Three assays including N1, N2 (US CDC Real-Time Reverse Transcription PCR Panel for Detection of SARS-CoV-2), and SC2 assay (US CDC Influenza SARS-CoV-2 Multiplex Assay) were implemented to detect SARS-CoV-2 in wastewater. The limit of blank and limit of detection for the three assays were experimentally determined. SARS-CoV-2 RNA concentrations were evaluated and compared through three statistical approaches, including Pearson and Spearman's rank correlations, Dynamic Time Warping, and vector autoregressive models. N1 and N2 demonstrated the highest correlation and most similar time series patterns. Conversely, N2 and SC2 assay demonstrated the lowest correlation and least similar time series patterns. N2 was identified as the optimal target to predict COVID-19 cases. This study presents a rigorous effort in evaluating and comparing SARS-CoV-2 RNA concentrations quantified with N1, N2, and SC2 assays and their interrelations and correlations with clinical cases. This study provides valuable insights into identifying the optimal target for monitoring COVID-19 through wastewater surveillance.

Wastewater-Based Surveillance Reveals the Effectiveness of the First COVID-19 Vaccination Campaigns in Assisted Living Facilities

The COVID-19 pandemic has disproportionately affected vulnerable populations, including residents of assisted living facilities (ALFs). This study investigates the impact of non-pharmaceutical interventions (NPIs) and mass vaccination campaigns on SARS-CoV-2 transmission dynamics within four ALFs in Maricopa County, Arizona, United States from January to April 2021. Initial observations reveal a significant SARS-CoV-2 prevalence in Maricopa County, with 7452 new COVID-19 cases reported on 4 January 2021. Wastewater surveillance indicates elevated viral loads within ALFs with peak concentrations reaching 1.35 × 107 genome copies/L at Facility 1 and 4.68 × 105 copies/L at Facility 2. The implementation of NPIs, including isolation protocols, resulted in a rapid decline in viral loads in wastewater. Following mass vaccination campaigns, viral loads reduced across all facilities, except Facility 4. Facility 1 demonstrated a mean viral load decrease from 1.65 × 106 copies/L to 1.04 × 103 copies/L post-vaccination, with a statistically significant U-statistic of 28.0 (p-value = 0.0027). Similar trends are observed in Facilities 2 and 3, albeit with varying degrees of statistical significance. In conclusion, this study provides evidence supporting the role of NPIs and vaccination campaigns in controlling SARS-CoV-2 transmission within ALFs.

Decay of RNA and infectious SARS-CoV-2 and murine hepatitis virus in wastewater

Wastewater-based epidemiology (WBE) has been an important tool for population surveillance during the COVID-19 pandemic and continues to play a key role in monitoring SARS-CoV-2 infection levels following reductions in national clinical testing schemes. Studies measuring decay profiles of SARS-CoV-2 in wastewater have underscored the value of WBE, however investigations have been hampered by high biosafety requirements for SARS-CoV-2 infection studies. Therefore, surrogate viruses with lower biosafety standards have been used for SARS-CoV-2 decay studies, such as murine hepatitis virus (MHV), but few studies have directly compared decay rates of both viruses. We compared the persistence of SARS-CoV-2 and MHV in wastewater, using 50 % tissue culture infectious dose (TCID50) and reverse transcription quantitative polymerase chain reaction (RT-qPCR) assays to assess infectious virus titre and viral gene markers, respectively. Infectious SARS-CoV-2 and MHV indicate similar endpoints, however observed early decay characteristics differed, with infectious SARS-CoV-2 decaying more rapidly than MHV. We find that MHV is an appropriate infectious virus surrogate for viable SARS-CoV-2, however inconsistencies exist in viral RNA decay parameters, indicating MHV may not be a suitable nucleic acid surrogate across certain temperature regimes. This study highlights the importance of sample preparation and the potential for decay rate overestimation in wastewater surveillance for SARS-CoV-2 and other pathogens.

Wastewater Surveillance Pilot at US Military Installations: Cost Model Analysis

Background

The COVID-19 pandemic highlighted the need for pathogen surveillance systems to augment both early warning and outbreak monitoring/control efforts. Community wastewater samples provide a rapid and accurate source of environmental surveillance data to complement direct patient sampling. Due to its global presence and critical missions, the US military is a leader in global pandemic preparedness efforts. Clinical testing for COVID-19 on US Air Force (USAF) bases (AFBs) was effective but costly with respect to direct monetary costs and indirect costs due to lost time. To remain operating at peak capacity, such bases sought a more passive surveillance option and piloted wastewater surveillance (WWS) at 17 AFBs to demonstrate feasibility, safety, utility, and cost-effectiveness from May 2021 to January 2022.

Objective

We model the costs of a wastewater program for pathogens of public health concern within the specific context of US military installations using assumptions based on the results of the USAF and Joint Program Executive Office for Chemical, Biological, Radiological and Nuclear Defense pilot program. The objective was to determine the cost of deploying WWS to all AFBs relative to clinical swab testing surveillance regimes.

Methods

A WWS cost projection model was built based on subject matter expert input and actual costs incurred during the WWS pilot program at USAF AFBs. Several SARS-CoV-2 circulation scenarios were considered, and the costs of both WWS and clinical swab testing were projected. Analysis was conducted to determine the break-even point and how a reduction in swab testing could unlock funds to enable WWS to occur in parallel.

Results

Our model confirmed that WWS is complementary and highly cost-effective when compared to existing alternative forms of biosurveillance. We found that the cost of WWS was between US $10.5-$18.5 million less expensive annually in direct costs as compared to clinical swab testing surveillance. When the indirect cost of lost work was incorporated, including lost work associated with required clinical swab testing, we estimated that over two-thirds of clinical swab testing could be maintained with no additional costs upon implementation of WWS.

Conclusions

Our results support the adoption of WWS across US military installations as part of a more comprehensive and early warning system that will enable adaptive monitoring during disease outbreaks in a more cost-effective manner than swab testing alone.

High-throughput microfluidic quantitative PCR system for the simultaneous detection of antibiotic resistance genes and bacterial and viral pathogens in wastewater

Comprehensive data on bacterial and viral pathogens of diarrhea and studies applying culture-independent methods for examining antibiotic resistance in wastewater are lacking. This study aimed to simultaneously quantify antibiotic resistance genes (ARGs), class 1 integron-integrase (int1), bacterial and viral pathogens of diarrhea, 16S rRNA, and other indicators using a high-throughput quantitative PCR (HT-qPCR) system. Thirty-six grab wastewater samples from a wastewater treatment plant in Japan, collected three times a month between August 2022 and July 2023, were centrifuged, followed by nucleic acid extraction, reverse transcription, and HT-qPCR. Fourteen targets were included, and HT-qPCR was performed on the Biomark X9™ System (Standard BioTools). For all qPCR assays, R2 was ≥0.978 and the efficiencies ranged from 90.5% to 117.7%, exhibiting high performance. Of the 36 samples, 20 (56%) were positive for Norovirus genogroup II (NoV-GII), whereas Salmonella spp. and Campylobacter jejuni were detected in 24 (67%) and Campylobacter coli in 13 (36%) samples, with mean concentrations ranging from 3.2 ± 0.8 to 4.7 ± 0.3 log10 copies/L. NoV-GII detection ratios and concentrations were higher in winter and spring. None of the pathogens of diarrhea correlated with acute gastroenteritis cases, except for NoV-GII, suggesting the need for data on specific bacterial infections to validate bacterial wastewater-based epidemiology (WBE). All samples tested positive for sul1, int1, and blaCTX-M, irrespective of season. The less explored blaNDM-1 showed a wide prevalence (>83%) and consistent abundance ranging from 4.3 ± 1.0 to 4.9 ± 0.2 log10 copies/L in all seasons. sul1 was the predominant ARG, whereas absolute abundances of 16S rRNA, int1, and blaCTX-M varied seasonally. int1 was significantly correlated with blaCTX-M in autumn and spring, whereas it showed no correlation with blaNDM-1, questioning the applicability of int1 as a sole indicator of overall resistance determinants. This study exhibited that the HT-qPCR system is pivotal for WBE.

Advancing Public Health Surveillance: Integrating Modeling and GIS in the Wastewater-Based Epidemiology of Viruses, a Narrative Review

This review article will present a comprehensive examination of the use of modeling, spatial analysis, and geographic information systems (GIS) in the surveillance of viruses in wastewater. With the advent of global health challenges like the COVID-19 pandemic, wastewater surveillance has emerged as a crucial tool for the early detection and management of viral outbreaks. This review will explore the application of various modeling techniques that enable the prediction and understanding of virus concentrations and spread patterns in wastewater systems. It highlights the role of spatial analysis in mapping the geographic distribution of viral loads, providing insights into the dynamics of virus transmission within communities. The integration of GIS in wastewater surveillance will be explored, emphasizing the utility of such systems in visualizing data, enhancing sampling site selection, and ensuring equitable monitoring across diverse populations. The review will also discuss the innovative combination of GIS with remote sensing data and predictive modeling, offering a multi-faceted approach to understand virus spread. Challenges such as data quality, privacy concerns, and the necessity for interdisciplinary collaboration will be addressed. This review concludes by underscoring the transformative potential of these analytical tools in public health, advocating for continued research and innovation to strengthen preparedness and response strategies for future viral threats. This article aims to provide a foundational understanding for researchers and public health officials, fostering advancements in the field of wastewater-based epidemiology.

Verifying the feasibility of wastewater-based epidemiological monitoring for the small catchment and sewage networks with significant pretreatment

Wastewater-based epidemiology (WBE) has emerged as a valuable tool for COVID-19 monitoring, especially as the frequency of clinical testing diminishes. Beyond COronaVIrus Disease 19 (COVID-19), the tool's versatility extends to addressing various public health concerns, including antibiotic resistance and drug consumption. However, the complexity of sewage systems introduces noise when measuring chemical tracer concentrations, potentially compromising their applicability for modeling. In our study, we detail the approach adopted to determine the concentration of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) ribonucleiec acid (RNA) in wastewater from the Ponte a Niccheri wastewater treatment plant in Tuscany (Italy), with a sample size of N = 13,935 inhabitants. The unique characteristics of this wastewater system, including mandatory pretreatment in septic tanks with extended retention times, the presence of a hospital for COVID-19 patients, and mixed sewage networks, posed additional challenges. Nevertheless, our results highlight a robust and significant correlation between our measurements and the number of infections within the wastewater treatment plant's catchment area at the time of sampling. A simple linear model also shows promising results in estimating the number of infected people within the area.

Interacting particle models on the impact of spatially heterogeneous human behavioral factors on dynamics of infectious diseases

Human behaviors have non-negligible impacts on spread of contagious disease. For instance, large-scale gathering and high mobility of population could lead to accelerated disease transmission, while public behavioral changes in response to pandemics may effectively reduce contacts and suppress the peak of the outbreak. In order to understand how spatial characteristics like population mobility and clustering interplay with epidemic outbreaks, we formulate a stochastic-statistical environment-epidemic dynamic system (SEEDS) via an agent-based biased random walk model on a two-dimensional lattice. The "popularity" and "awareness" variables are taken into consideration to capture human natural and preventive behavioral factors, which are assumed to guide and bias agent movement in a combined way. It is found that the presence of the spatial heterogeneity, like social influence locality and spatial clustering induced by self-aggregation, potentially suppresses the contacts between agents and consequently flats the epidemic curve. Surprisedly, disease responses might not necessarily reduce the susceptibility of informed individuals and even aggravate disease outbreak if each individual responds independently upon their awareness. The disease control is achieved effectively only if there are coordinated public-health interventions and public compliance to these measures. Therefore, our model may be useful for quantitative evaluations of a variety of public-health policies.

A narrative review of wastewater surveillance: pathogens of concern, applications, detection methods, and challenges

Introduction

The emergence and resurgence of pathogens have led to significant global health challenges. Wastewater surveillance has historically been used to track water-borne or fecal-orally transmitted pathogens, providing a sensitive means of monitoring pathogens within a community. This technique offers a comprehensive, real-time, and cost-effective approach to disease surveillance, especially for diseases that are difficult to monitor through individual clinical screenings.

Methods

This narrative review examines the current state of knowledge on wastewater surveillance, emphasizing important findings and techniques used to detect potential pathogens from wastewater. It includes a review of literature on the detection methods, the pathogens of concern, and the challenges faced in the surveillance process.

Results

Wastewater surveillance has proven to be a powerful tool for early warning and timely intervention of infectious diseases. It can detect pathogens shed by asymptomatic and pre-symptomatic individuals, providing an accurate population-level view of disease transmission. The review highlights the applications of wastewater surveillance in tracking key pathogens of concern, such as gastrointestinal pathogens, respiratory pathogens, and viruses like SARS-CoV-2.

Discussion

The review discusses the benefits of wastewater surveillance in public health, particularly its role in enhancing existing systems for infectious disease surveillance. It also addresses the challenges faced, such as the need for improved detection methods and the management of antimicrobial resistance. The potential for wastewater surveillance to inform public health mitigation strategies and outbreak response protocols is emphasized.

Conclusion

Wastewater surveillance is a valuable tool in the fight against infectious diseases. It offers a unique perspective on the spread and evolution of pathogens, aiding in the prevention and control of disease epidemics. This review underscores the importance of continued research and development in this field to overcome current challenges and maximize the potential of wastewater surveillance in public health.

Wastewater-based epidemiology surveillance as an early warning system for SARS-CoV-2 in Indonesia

BACKGROUND

Wastewater-based epidemiology (WBE) surveillance has been proposed as an early warning system (EWS) for community SARS-CoV-2 transmission. However, there is limited data from low-and middle-income countries (LMICs). This study aimed to assess the ability of WBE surveillance to detect SARS-CoV-2 in formal and informal environments in Indonesia using different methods of sample collection, to compare WBE data with patterns of clinical cases of COVID-19 within the relevant communities, and to assess the WBE potential to be used as an EWS for SARS-CoV-2 outbreaks within a community.

MATERIALS AND METHODS

We conducted WBE surveillance in three districts in Yogyakarta province, Indonesia, over eleven months (27 July 2021 to 7 January 2022 [Delta wave]; 18 January to 3 June 2022 [Omicron wave]). Water samples using grab, and/or passive sampling methods and soil samples were collected either weekly or fortnightly. RNA was extracted from membrane filters from processed water samples and directly from soil. Reverse-transcription quantitative real-time polymerase chain reaction (RT-qPCR) was performed to detect the SARS-CoV-2 N and ORF1ab genes.

RESULTS

A total of 1,582 samples were collected. Detection rates of SARS-CoV-2 in wastewater reflected the incidence of community cases, with rates of 85% at the peak to 2% at the end of the Delta wave and from 94% to 11% during the Omicron wave. A 2-week lag time was observed between the detection of SARS-CoV-2 in wastewater and increasing cases in the corresponding community.

CONCLUSION

WBE surveillance for SARS-CoV-2 in Indonesia was effective in monitoring patterns of cases of COVID-19 and served as an early warning system, predicting the increasing incidence of COVID-19 cases in the community.

Graphene Multiplexed Sensor for Point-of-Need Viral Wastewater-Based Epidemiology

Wastewater-based epidemiology (WBE) can help mitigate the spread of respiratory infections through the early detection of viruses, pathogens, and other biomarkers in human waste. The need for sample collection, shipping, and testing facilities drives up the cost of WBE and hinders its use for rapid detection and isolation in environments with small populations and in low-resource settings. Given the ubiquitousness and regular outbreaks of respiratory syncytial virus, SARS-CoV-2, and various influenza strains, there is a rising need for a low-cost and easy-to-use biosensing platform to detect these viruses locally before outbreaks can occur and monitor their progression. To this end, we have developed an easy-to-use, cost-effective, multiplexed platform able to detect viral loads in wastewater with several orders of magnitude lower limit of detection than that of mass spectrometry. This is enabled by wafer-scale production and aptamers preattached with linker molecules, producing 44 chips at once. Each chip can simultaneously detect four target analytes using 20 transistors segregated into four sets of five for each analyte to allow for immediate statistical analysis. We show our platform's ability to rapidly detect three virus proteins (SARS-CoV-2, RSV, and Influenza A) and a population normalization molecule (caffeine) in wastewater. Going forward, turning these devices into hand-held systems would enable wastewater epidemiology in low-resource settings and be instrumental for rapid, local outbreak prevention.

Repurposing Sewage and Toilet Systems: Environmental, Public Health, and Person-Centered Healthcare Applications

Global terrestrial water supplies are rapidly depleting due to the consequences of climate change. Water scarcity results in an inevitable compromise of safe hygiene and sanitation practices, leading to the transmission of water-borne infectious diseases, and the preventable deaths of over 800.000 people each year. Moreover, almost 500 million people lack access to toilets and sanitation systems. Ecosystems are estimated to be contaminated by 6.2 million tons of nitrogenous products from human wastewater management practices. It is therefore imperative to transform toilet and sewage systems to promote equitable access to water and sanitation, improve public health, conserve water, and protect ecosystems. Here, the integration of emerging technologies in toilet and sewage networks to repurpose toilet and wastewater systems is reviewed. Potential applications of these systems to develop sustainable solutions to environmental challenges, promote public health, and advance person-centered healthcare are discussed.

Group A Streptococcus pyogenes in wastewater: Applicability of wastewater-based epidemiology for monitoring the prevalence of GAS pharyngitis during the late COVID-19 pandemic phase

Streptococcus pyogenes, Group A Streptococcus (GAS), is a human pathogen that causes a spectrum of diseases from mild to severe, including GAS pharyngitis, a common acute respiratory disease in developed countries. Although wastewater-based epidemiology (WBE) has been extensively used to monitor viral pathogens such as severe acute respiratory syndrome coronavirus 2, its applicability to S. pyogenes remains unexplored. This study was conducted to investigate the feasibility of detecting and quantifying S. pyogenes in wastewater by quantitative polymerase chain reaction (qPCR) and evaluate the applicability of WBE for monitoring the prevalence of GAS pharyngitis. A total of 52 grab influent samples were collected from a wastewater treatment plant in Japan once a week between March 2023 and February 2024. The samples were centrifuged, followed by nucleic acid extraction and qPCR for the S. pyogenes-specific genes speB and spy1258. Of the 52 samples, 90 % and 81 % were positive for speB and spy1258 genes, respectively, indicating the feasibility of S. pyogenes for wastewater surveillance. However, the percentage of quantifiable samples for speB gene was significantly higher in winter than in spring and summer. Similarly, the concentrations of both genes in wastewater samples were significantly higher in winter (speB, 4.1 ± 0.27 log10 copies/L; spy1258, 4.1 ± 0.28 log10 copies/L; One-way ANOVA, p < 0.01) than in spring and summer. Higher concentrations and detection ratios of S. pyogenes genes were observed during increased GAS pharyngitis cases in the catchment. Significant moderate correlations were observed between target gene concentrations and reported GAS pharyngitis cases. This study enhances the understanding role of WBE in monitoring and managing infectious diseases within communities.

Wastewater-Based Epidemiology for SARS-CoV-2 in Northern Italy: A Spatiotemporal Model

The study investigated the application of Wastewater-Based Epidemiology (WBE) as a tool for monitoring the SARS-CoV-2 prevalence in a city in northern Italy from October 2021 to May 2023. Based on a previously used deterministic model, this study proposed a variation to account for the population characteristics and virus biodegradation in the sewer network. The model calculated virus loads and corresponding COVID-19 cases over time in different areas of the city and was validated using healthcare data while considering viral mutations, vaccinations, and testing variability. The correlation between the predicted and reported cases was high across the three waves that occurred during the period considered, demonstrating the ability of the model to predict the relevant fluctuations in the number of cases. The population characteristics did not substantially influence the predicted and reported infection rates. Conversely, biodegradation significantly reduced the virus load reaching the wastewater treatment plant, resulting in a 30% reduction in the total virus load produced in the study area. This approach can be applied to compare the virus load values across cities with different population demographics and sewer network structures, improving the comparability of the WBE data for effective surveillance and intervention strategies.

Heavy metal contamination in Sungai Petani, Malaysia: a wastewater-based epidemiology study

The aim of this study was to investigate the use of wastewater-based epidemiology (WBE) to estimate heavy metal exposure in Sungai Petani, Malaysia. Atomic absorption spectroscopy was used to detect copper (Cu), nickel (Ni), zinc (Zn), iron (Fe), and cadmium (Cd) in wastewater from eight sewage treatment facilities in Sungai Petani in January 2022. The heavy metal concentrations were measured in both influent and effluent, and the mean concentrations in the wastewater were found to be in the following order: Fe > Ni > Zn > Cd > Cu, with a 100% detection frequency. The results of WBE estimation showed that Fe, Ni, and Zn had the highest estimated per population exposure levels, while Cd had the lowest. Compared to a similar study conducted in Penang, Malaysia, all metals except Cu were found to have higher concentrations in Sungai Petani, even though it is a non-industrial district. These findings highlight the importance of addressing heavy metal contamination in Sungai Petani and implementing effective risk management and prevention strategies.

Measuring SARS-CoV-2 RNA concentrations in neighborhood wastewater

Longitudinal wastewater sampling during the COVID-19 pandemic was an important aspect of disease surveillance, adding to a more complete understanding of infection dynamics and providing important data for community public health monitoring and intervention planning. This was largely accomplished by testing SARS-CoV-2 RNA concentrations in samples from municipal wastewater treatment plants (WWTPs). We evaluated the utility of testing for virus levels upstream from WWTP within the residential neighborhoods that feed into the WWTP. We propose that monitoring virus dynamics across residential neighborhoods could reveal important public health-relevant information about community sub-group heterogeneity in virus concentrations. PRINCIPAL RESULTS: Virus concentration patterns display heterogeneity within neighborhoods and between neighborhoods over time. Sewage SARS-CoV-2 RNA concentrations as measured by RT-qPCR also corresponded closely to verified COVID-19 infection counts within individual neighborhoods. More importantly, our data suggest the loss of disease-relevant public health information when sampling occurs only at the level of WWTP instead of upstream in neighborhoods. Spikes in SARS-CoV-2 RNA concentrations in neighborhoods are often masked by dilution from other neighborhoods in the WWTP samples. MAJOR CONCLUSIONS: Wastewater-based epidemiology (WBE) employed at WWTP reliably detects SARS-CoV-2 in a city-sized population but provides less actionable public health information about neighborhoods experiencing greater viral infection and disease. Neighborhood sewershed sampling reveals important population-based information about local virus dynamics and improves opportunities for public health intervention. Longitudinally employed, neighborhood sewershed surveillance may provide a 3-6 day early warning of SARS-CoV-2 infection spikes and, importantly, highly specific information on subpopulations in a community particularly at higher risk at different points in time. Sampling in neighborhoods may thus provide timely and cost-saving information for targeted interventions within communities.

Wastewater sequencing as a powerful tool to reveal SARS-CoV-2 variant introduction and spread in French Guiana, South America

The origin of introduction of a new pathogen in a country, the evolutionary dynamics of an epidemic within a country, and the role of cross-border areas on pathogen dynamics remain complex to disentangle and are often poorly understood. For instance, cross-border areas represent the ideal location for the sharing of viral variants between countries, with international air travel, land travel and waterways playing an important role in the cross-border spread of infectious diseases. Unfortunately, monitoring the point of entry and the evolutionary dynamics of viruses in space and time within local populations remain challenging. Here we tested the efficiency of wastewater-based epidemiology and genotyping in monitoring Covid-19 epidemiology and SARS-CoV-2 variant dynamics in French Guiana, a tropical country located in South America. Our results suggest that wastewater-based epidemiology and genotyping are powerful tools to monitor variant introduction and disease evolution within a tropical country but the inclusion of both clinical and wastewater samples could still improve our understanding of genetic diversity co-circulating. Wastewater sequencing also revealed the cryptic transmission of SARS-CoV-2 variants within the country. Interestingly, we found some amino acid changes specific to the variants co-circulating in French Guiana, suggesting a local evolution of the SARS-CoV-2 variants after their introduction. More importantly, our results showed that the proximity to bordering countries was not the origin of the emergence of the French Guianese B.1.160.25 variant, but rather that this variant emerged from an ancestor B.1.160 variant introduced by European air plane travelers, suggesting thus that air travel remains a significant risk for cross-border spread of infectious diseases. Overall, we suggest that wastewater-based epidemiology and genotyping provides a cost effective and non-invasive approach for pathogen monitoring and an early-warning tool for disease emergence and spread within a tropical country.

Campus source to sink wastewater surveillance of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)

Wastewater-based surveillance (WBS) offers an aggregate, and cost-effective approach for tracking infectious disease outbreak prevalence within communities, that provides data on community health complementary to individual clinical testing. This study reports on a 16-month WBS initiative on a university campus in England, UK, assessing the presence of SARS-CoV-2 in sewers from large buildings, downstream sewer locations, raw wastewater, partially treated and treated effluents. Key findings include the detection of the Alpha (B.1.1.7) variant in wastewater, with 70 % of confirmed campus cases correlating with positive wastewater samples. Notably, ammonium nitrogen (NH4-N) levels showed a positive correlation (ρ = 0.543, p < 0.01) with virus levels at the large building scale, a relationship not observed at the sewer or wastewater treatment works (WWTW) levels due to dilution. The WWTW was compliant to wastewater standards, but the secondary treatment processes were not efficient for virus removal as SARS-CoV-2 was consistently detected in treated discharges. Tools developed through WBS can also be used to enhance traditional environmental monitoring of aquatic systems. This study provides a detailed source-to-sink evaluation, emphasizing the critical need for the widespread application and improvement of WBS. It showcases WBS utility and reinforces the ongoing challenges posed by viruses to receiving water quality.

SARS-CoV-2 wastewater surveillance in the Czech Republic: Spatial and temporal differences in SARS-CoV-2 RNA concentrations and relationship to clinical data and wastewater parameters

This study presents the results of systematic wastewater monitoring of SARS-CoV-2 RNA and basic wastewater parameters from four different wastewater treatment plants (WWTPs) in the Czech Republic over the 2020-2022 epidemic. Two-step reverse-transcription quantitative PCR targeting genes encoding the N and Nsp12 proteins was employed to detect SARS-CoV-2 RNA loading in 420 wastewater samples. The results obtained were used to evaluate the potential of wastewater analysis for describing the epidemiological situation in cities of different sizes and determining temporal differences based on the prevailing SARS-CoV-2 variant. Strong correlations between the number of active and hospitalised COVID-19 cases in each WWTP catchment area and the concentration of SARS-CoV-2 RNA detected in the wastewater clearly demonstrated the suitability of this wastewater-based epidemiological approach for WWTPs of different sizes and characteristics, despite differences in SARS-CoV-2 variant waves, with some WWTPs showing high predictive potential. This study demonstrated on the data from the Czech Republic that targeted systematic monitoring of wastewater provides sufficiently robust data for surveillance of viral loads in sample populations, and thus contributes to preventing the spread of infection and subsequent introduction of appropriate measures.

Tracking SARS-CoV-2 variants of concern in wastewater: an assessment of nine computational tools using simulated genomic data

Wastewater-based surveillance (WBS) is an important epidemiological and public health tool for tracking pathogens across the scale of a building, neighbourhood, city, or region. WBS gained widespread adoption globally during the SARS-CoV-2 pandemic for estimating community infection levels by qPCR. Sequencing pathogen genes or genomes from wastewater adds information about pathogen genetic diversity, which can be used to identify viral lineages (including variants of concern) that are circulating in a local population. Capturing the genetic diversity by WBS sequencing is not trivial, as wastewater samples often contain a diverse mixture of viral lineages with real mutations and sequencing errors, which must be deconvoluted computationally from short sequencing reads. In this study we assess nine different computational tools that have recently been developed to address this challenge. We simulated 100 wastewater sequence samples consisting of SARS-CoV-2 BA.1, BA.2, and Delta lineages, in various mixtures, as well as a Delta-Omicron recombinant and a synthetic 'novel' lineage. Most tools performed well in identifying the true lineages present and estimating their relative abundances and were generally robust to variation in sequencing depth and read length. While many tools identified lineages present down to 1 % frequency, results were more reliable above a 5 % threshold. The presence of an unknown synthetic lineage, which represents an unclassified SARS-CoV-2 lineage, increases the error in relative abundance estimates of other lineages, but the magnitude of this effect was small for most tools. The tools also varied in how they labelled novel synthetic lineages and recombinants. While our simulated dataset represents just one of many possible use cases for these methods, we hope it helps users understand potential sources of error or bias in wastewater sequencing analysis and to appreciate the commonalities and differences across methods.

Early Detection of Novel SARS-CoV-2 Variants from Urban and Rural Wastewater through Genome Sequencing and Machine Learning

Genome sequencing from wastewater has emerged as an accurate and cost-effective tool for identifying SARS-CoV-2 variants. However, existing methods for analyzing wastewater sequencing data are not designed to detect novel variants that have not been characterized in humans. Here, we present an unsupervised learning approach that clusters co-varying and time-evolving mutation patterns leading to the identification of SARS-CoV-2 variants. To build our model, we sequenced 3,659 wastewater samples collected over a span of more than two years from urban and rural locations in Southern Nevada. We then developed a multivariate independent component analysis (ICA)-based pipeline to transform mutation frequencies into independent sources with co-varying and time-evolving patterns and compared variant predictions to >5,000 SARS-CoV-2 clinical genomes isolated from Nevadans. Using the source patterns as data-driven reference "barcodes", we demonstrated the model's accuracy by successfully detecting the Delta variant in late 2021, Omicron variants in 2022, and emerging recombinant XBB variants in 2023. Our approach revealed the spatial and temporal dynamics of variants in both urban and rural regions; achieved earlier detection of most variants compared to other computational tools; and uncovered unique co-varying mutation patterns not associated with any known variant. The multivariate nature of our pipeline boosts statistical power and can support accurate and early detection of SARS-CoV-2 variants. This feature offers a unique opportunity for novel variant and pathogen detection, even in the absence of clinical testing.

Human viral pathogens in the wastewater-source water-drinking water continuum: Evidence, health risks, and lessons for future outbreaks in low-income settings

Human viral pathogens, including SARS-CoV-2 continue to attract public and research attention due to their disruption of society, global health, and the economy. Several earlier reviews have investigated the occurrence and fate of SARS-CoV-2 in wastewater, and the potential to use such data in wastewater-based epidemiology. However, comprehensive reviews tracking SARS-CoV-2 and other viral pathogens in the wastewater-water-drinking water continuum and the associated risk assessment are still lacking. Therefore, to address this gap, the present paper makes the following contributions: (1) critically examines the early empirical results to highlight the occurrence and stability of SARS-CoV-2 in the wastewater-source water-drinking water continuum, (2) discusses the anthropogenic and hydro(geo)logical processes controlling the circulation of SARS-CoV-2 in the wastewater-source water-drinking water continuum, (3) discusses the risky behaviour, drivers and high-risk settings in the wastewater-source water-drinking water continuum, (4) uses the available empirical data on SARS-CoV-2 occurrence in the wastewater-source water-drinking water continuum to discuss human health risks from multiple exposure pathways, gendered aspects of SARS-CoV-2 transmission via shared on-site sanitation systems, and (5) develops and risk mitigation strategy based on the available empirical evidence and quantitative human risk assessment data. Finally, it presents a comprehensive research agenda on SARS-CoV-2/COVID-19 to guide the mitigation of future similar outbreaks in low-income settings.

First Molecular Detection of SARS-CoV-2 in Sewage and Wastewater in Ghana

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is shed in the stool of infected individuals and can be detected in sewage and wastewater contaminated with infected stool. This study is aimed at detecting the virus and its potential survival in sewage and wastewater in Ghana. The cross-sectional study included samples from 16 validated environmental surveillance sites in 7 regions of Ghana. A total of 354 samples composed of wastewater (280) and sewage (74) were collected from November 2020 to November 2022. Overall, 17% of the samples were positive for SARS-CoV-2 by real-time PCR, with 6% in sewage and 11% in wastewater. The highest number of positive samples was collected from the Greater Accra Region (7.3%) with the least recorded in the Bono East Region (0.6%). Further characterization of the positive samples using the next-generation sequencing (NGS) approach yielded two variants: Alpha (B.1.1.7) and Delta (AY.36). Attempts to isolate SARS-CoV-2 in the Vero cell line were not successful probably due to the low viral load concentrations (Ct values > 35) or prolonged exposure to high temperatures rendering the virus noninfectious. Our findings suggest that SARS-CoV-2 RNA in sewage and wastewater may not be infectious, but the prevalence shows that the virus persists in the communities within Ghana.

Influences of 23 different equations used to calculate gene copies of SARS-CoV-2 during wastewater-based epidemiology

Following the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in late 2019, the use of wastewater-based surveillance (WBS) has increased dramatically along with associated infrastructure globally. However, due to the global nature of its application, and various workflow adaptations (e.g., sample collection, water concentration, RNA extraction kits), numerous methods for back-calculation of gene copies per volume (gc/L) of sewage have also emerged. Many studies have considered the comparability of processing methods (e.g., water concentration, RNA extraction); however, for equations used to calculate gene copies in a wastewater sample and subsequent influences on monitoring viral trends in a community and its association with epidemiological data, less is known. Due to limited information on how many formulas exist for the calculation of SARS-CoV-2 gene copies in wastewater, we initially attempted to quantify how many equations existed in the referred literature. We identified 23 unique equations, which were subsequently applied to an existing wastewater dataset. We observed a range of gene copies based on use of different equations, along with variability of AUC curve values, and results from correlation and regression analyses. Though a number of individual laboratories appear to have independently converged on a similar formula for back-calculation of viral load in wastewater, and share similar relationships with epidemiological data, differential influences of various equations were observed for variation in PCR volumes, RNA extraction volumes, or PCR assay parameters. Such observations highlight challenges when performing comparisons among WBS studies when numerous methodologies and back-calculation methods exist. To facilitate reproducibility among studies, the different gc/L equations were packaged as an R Shiny app, which provides end users the ability to investigate variability within their datasets and support comparisons among studies.

A modeling pipeline to relate municipal wastewater surveillance and regional public health data

As COVID-19 becomes endemic, public health departments benefit from improved passive indicators, which are independent of voluntary testing data, to estimate the prevalence of COVID-19 in local communities. Quantification of SARS-CoV-2 RNA from wastewater has the potential to be a powerful passive indicator. However, connecting measured SARS-CoV-2 RNA to community prevalence is challenging due to the high noise typical of environmental samples. We have developed a generalized pipeline using in- and out-of-sample model selection to test the ability of different correction models to reduce the variance in wastewater measurements and applied it to data collected from treatment plants in the Chicago area. We built and compared a set of multi-linear regression models, which incorporate pepper mild mottle virus (PMMoV) as a population biomarker, Bovine coronavirus (BCoV) as a recovery control, and wastewater system flow rate into a corrected estimate for SARS-CoV-2 RNA concentration. For our data, models with BCoV performed better than those with PMMoV, but the pipeline should be used to reevaluate any new data set as the sources of variance may change across locations, lab methods, and disease states. Using our best-fit model, we investigated the utility of RNA measurements in wastewater as a leading indicator of COVID-19 trends. We did this in a rolling manner for corrected wastewater data and for other prevalence indicators and statistically compared the temporal relationship between new increases in the wastewater data and those in other prevalence indicators. We found that wastewater trends often lead other COVID-19 indicators in predicting new surges.

Fate-and-transport modeling of SARS-CoV-2 for rural wastewater-based epidemiology application benefit

Wastewater-based epidemiology (WBE) for the detection of agents of concern such as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been prevalent in literature since 2020. The majority of reported research focuses on large urban centers with few references to rural communities. In this research the EPA-Storm Water Management Model (EPA-SWMM) software was used to describe a small sewershed and identify the effects of temperature, temperature-affected decay rate, flow rate, flush time, fecal shedding rate, and historical infection rates during the spread of the Omicron variant of the SARS-CoV-2 virus within the sewershed. Due to the sewershed's relative isolation from the rest of the city, its wastewater quality behavior is similar to a rural sewershed. The model was used to assess city wastewater sampling campaigns to best appropriate field and or lab equipment when sampling wastewater. An important aspect of the assessment was the comparison of SARS-CoV-2 quantification methods with specifically between a traditional microbiological lab (practical quantitation limit, PQL, 1 GC/mL) versus what can be known from a field method (PQL 10 GC/mL). Understanding these monitoring choices will help rural communities make decisions on how to best implement the collection and testing for WBE agents of concern. An important outcome of this work is the knowledge that it is possible to simulate a WBE agent of concern with reasonable precision, if uncertainties are incorporated into model sensitivity. These ideas could form the basis for future mixed monitoring-modeling studies that will enhance its application and therefore adoption of WBE techniques in communities of many sizes and financial means.