Decay of four enteric pathogens and implications to wastewater-based epidemiology: Effects of temperature and wastewater dilutions

Tracking Chlamydia and Syphilis in the Detroit Metro Area by Molecular Analysis of Environmental Samples

This paper describes one of the first studies applying wastewater surveillance to monitor Chlamydia and Syphilis and back-estimate infections in the community, based on bacterial shedding and wastewater surveillance data. Molecular biology laboratory methods were optimized, and a workflow was designed to implement wastewater surveillance tracking Chlamydia and Syphilis in the Detroit metro area (DMA), one of the most populous metropolitan areas in the U.S. Untreated composite wastewater samples were collected weekly from the three main interceptors that service DMA, which collect wastewater and discharge it to the Great Lakes Water Authority Water Resource Recovery Facility. Additionally, untreated wastewater was also collected from street manholes in three neighborhood sewersheds in Wayne, Macomb, and Oakland counties. Centrifugation, DNA extraction, and ddPCR methods were optimized and performed, targeting Chlamydia trachomatis and Treponema pallidum, the causative agents of Chlamydia and Syphilis, respectively. The limit of blank and limit of detection methods were determined experimentally for both targets. Both targets were detected and monitored in wastewater between December 25th, 2023, and April 22nd, 2024. The magnitudes of C. trachomatis and T. pallidum concentrations observed in neighborhood sewersheds were higher as compared to the concentrations observed in the interceptors. Infections of Chlamydia and Syphilis were back-estimated through an optimized formula based on shedding dynamics and wastewater surveillance data, which indicated potentially underreported conditions relative to publicly available clinical data.

Making waves: The benefits and challenges of responsibly implementing wastewater-based surveillance for rural communities

The sampling and analysis of sewage for pathogens and other biomarkers offers a powerful tool for monitoring and understanding community health trends and potentially predicting disease outbreaks. Since the early months of the COVID-19 pandemic, the use of wastewater-based testing for public health surveillance has increased markedly. However, these efforts have focused on urban and peri‑urban areas. In most rural regions of the world, healthcare service access is more limited than in urban areas, and rural public health agencies typically have less disease outcome surveillance data than their urban counterparts. The potential public health benefits of wastewater-based surveillance for rural communities are therefore substantial - though so too are the methodological and ethical challenges. For many rural communities, population dynamics and insufficient, aging, and inadequately maintained wastewater collection and treatment infrastructure present obstacles to the reliable and responsible implementation of wastewater-based surveillance. Practitioner observations and research findings indicate that for many rural systems, typical implementation approaches for wastewater-based surveillance will not yield sufficiently reliable or actionable results. We discuss key challenges and potential strategies to address them. However, to support and expand the implementation of responsible, reliable, and ethical wastewater-based surveillance for rural communities, best practice guidelines and standards are needed.

Predicting wastewater treatment plant influent in mixed, separate, and combined sewers using nearby surface water discharge for better wastewater-based epidemiology sampling design

For wastewater sample collection approaches supporting public health applications, few high hydrologic activity normalizing guidelines currently consider readily available environmental flow data that may earlier capture information regarding periods of influent mixing and dilution of wastewater with groundwater and runoff. This study aimed to identify wastewater sampling rules for high hydrological activity events, allowing for an earlier decision point in the control of dilution before sample collection. We defined the sampling rules via data-driven models (Random Forest and linear regression) using environmental data (i.e., wastewater treatment facility influent rates, nearby stream discharge flow, and precipitation). These models were applied to five treatment plants in Jefferson County, Kentucky (USA) in mixed, separate, and combined sewers with different population sizes. We proposed cutoffs of 10 %, 25 %, and 50 % flow conditions for orientation towards public health samples. The results showed a strong nonlinear relationship between nearby stream discharge and treatment facility flow rates, which was used to infer the hydrological conditions that produce high volumes of diluted wastewater in the sewer system. Accumulated Local Effects and SHapley Additive exPlanations aided in deciphering the relationship between the predictors and response variables of the Random Forest models. The influent rate to the treatment plant from the previous day and two USGS stream gages were needed to adequately predict the degree of infiltration and inflow mixing on a given day. Surface water discharge data can be used to provide an earlier workflow decision point during wet weather periods to improve understanding of flow conditions for wastewater-based epidemiological studies to inform laboratory analysis and data interpretation. Not only total flow, but also the specific proportions of infiltration and inflow to wastewater volume in influent should be considered when analyzing data for normalization purposes, and our method provides a starting point for doing so rapidly and at low cost.

Characterizing Spatial Information Loss for Wastewater Surveillance Using crAssphage: Effect of Decay, Temperature, and Population Mobility

Populations contribute information about their health status to wastewater. Characterizing how that information degrades in transit to wastewater sampling locations (e.g., wastewater treatment plants and pumping stations) is critical to interpret wastewater responses. In this work, we statistically estimate the loss of information about fecal contributions to wastewater from spatially distributed populations at the census block group resolution. This was accomplished with a hydrologically and hydraulically influenced spatial statistical approach applied to crAssphage (Carjivirus communis) load measured from the influent of four wastewater treatment plants in Hamilton County, Ohio. We find that we would expect to observe a 90% loss of information about fecal contributions from a given census block group over a travel time of 10.3 h. This work demonstrates that a challenge to interpreting wastewater responses (e.g., during wastewater surveillance) is distinguishing between a distal but large cluster of contributions and a near but small contribution. This work demonstrates new modeling approaches to improve measurement interpretation depending on sewer network and wastewater characteristics (e.g., geospatial layout, temperature variability, population distribution, and mobility). This modeling can be integrated into standard wastewater surveillance methods and help to optimize sewer sampling locations to ensure that different populations (e.g., vulnerable and susceptible) are appropriately represented.

Wastewater-based epidemiology of Campylobacter spp.: A systematic review and meta-analysis of influent, effluent, and removal of wastewater treatment plants

Campylobacter spp. is one of the four leading causes of diarrhoeal diseases worldwide, which are generally mild but can be fatal in children, the elderly, and immunosuppressed persons. The existing disease surveillance for Campylobacter infections is usually based on untimely clinical reports. Wastewater surveillance or wastewater-based epidemiology (WBE) has been developed for the early warning of disease outbreaks and the detection of the emerging new variants of human pathogens, especially after the global pandemic of COVID-19. However, the WBE monitoring of Campylobacter infections in communities is rare due to a few large data gaps. This study is a meta-analysis and systematic review of the prevalence of Campylobacter spp. in various wastewater samples, primarily the influent of wastewater treatment plants. The results showed that the overall prevalence of Campylobacter spp. was 53.26 % in influent wastewater and 52.97 % in all types of wastewater samples. The mean concentration in the influent was 3.31 ± 0.39 log10 gene copies or most probable number (MPN) per 100 mL. The detection method combining culture and PCR yielded the highest positive rate of 90.86 %, while RT-qPCR and qPCR were the two most frequently used quantification methods. In addition, the Campylobacter concentration in influent wastewater showed a seasonal fluctuation, with the highest concentration in the autumn at 3.46 ± 0.41 log10 gene copies or MPN per 100 mL. Based on the isolates of all positive samples, Campylobacter jejuni (62.34 %) was identified as the most prevalent species in wastewater, followed by Campylobacter coli (30.85 %) and Campylobacter lari (4.4 %). These findings provided significant data to further develop and optimize the wastewater surveillance of Campylobacter spp. infections. In addition, large data gaps were found in the decay of Campylobacter spp. in wastewater, indicating insufficient research on the persistence of Campylobacter spp. in wastewater.

Longitudinal wastewater surveillance of four key pathogens during an unprecedented large-scale COVID-19 outbreak in China facilitated a novel strategy for addressing public health priorities-A proof of concept study

Wastewater-based epidemiology (WBE) is a promising tool for monitoring the spread of SARS-CoV-2 and other pathogens, providing a novel public health strategy to combat disease. In this study, we first analysed nationwide reports of infectious diseases and selected Salmonella, norovirus, and influenza A virus (IAV) as prioritized targets apart from SARS-CoV-2 for wastewater surveillance. Next, the decay rates of Salmonella, norovirus, and IAV in wastewater at various temperatures were established to obtain corrected pathogen concentrations in sewage. We then monitored the concentrations of these pathogens in wastewater treatment plant (WWTP) influents in three cities, establishing a prediction model to estimate the number of infected individuals based on the mass balance between total viral load in sewage and individual viral shedding. From October 2022 to March 2023, we conducted multipathogen wastewater surveillance (MPWS) in a WWTP serving one million people in Xi'an City, monitoring the concentration dynamics of SARS-CoV-2, Salmonella, norovirus, and IAV in sewage. The infection peaks of each pathogen were different, with Salmonella cases and sewage concentration declining from October to December 2022 and only occasionally detected thereafter. The SARS-CoV-2 concentration rapidly increased from December 5th, peaked on December 26th, and then quickly decreased until the end of the study. Norovirus and IAV were detected in wastewater from January to March 2023, peaking in February and March, respectively. We used the prediction models to estimate the rate of SARS-CoV-2 infection in Xi'an city, with nearly 90 % of the population infected in urban regions. There was no significant difference between the predicted and actual number of hospital admissions for IAV. We also accurately predicted the number of norovirus cases relative to the reported cases. Our findings highlight the importance of wastewater surveillance in addressing public health priorities, underscoring the need for a novel workflow that links the prediction results of populations with public health interventions and allocation of medical resources at the community level. This approach would prevent medical resource panic squeezes, reduce the severity and mortality of patients, and enhance overall public health outcomes.

The Protective Effect of Virus Capsids on RNA and DNA Virus Genomes in Wastewater

Virus concentrations measured in municipal wastewater help inform both the water treatment necessary to protect human health and wastewater-based epidemiology. Wastewater measurements are typically PCR-based, and interpreting gene copy concentrations requires an understanding of the form and stability of the nucleic acids. Here, we study the persistence of model virus genomes in wastewater, the protective effects provided by the virus capsids, and the relative decay rates of the genome and infectious viruses. In benchtop batch experiments in wastewater influent at 25 °C, extraviral (+)ssRNA and dsDNA amplicons degraded by 90% within 15-19 min and 1.6-1.9 h, respectively. When encapsidated, the T90 for MS2 (+)ssRNA increased by 424× and the T90 for T4 dsDNA increased by 52×. The (+)ssRNA decay rates were similar for a range of amplicon sizes. For our model phages MS2 and T4, the nucleic acid signal in untreated wastewater disappeared shortly after the viruses lost infectivity. Combined, these results suggest that most viral genome copies measured in wastewater are encapsidated, that measured concentrations are independent of assay amplicon sizes, and that the virus genome decay rates of nonenveloped (i.e., naked) viruses are similar to inactivation rates. These findings are valuable for the interpretation of wastewater virus measurements.

Wastewater surveillance beyond COVID-19: a ranking system for communicable disease testing in the tri-county Detroit area, Michigan, USA

Introduction

Throughout the coronavirus disease 2019 (COVID-19) pandemic, wastewater surveillance has been utilized to monitor the disease in the United States through routine national, statewide, and regional monitoring projects. A significant canon of evidence was produced showing that wastewater surveillance is a credible and effective tool for disease monitoring. Hence, the application of wastewater surveillance can extend beyond monitoring SARS-CoV-2 to encompass a diverse range of emerging diseases. This article proposed a ranking system for prioritizing reportable communicable diseases (CDs) in the Tri-County Detroit Area (TCDA), Michigan, for future wastewater surveillance applications at the Great Lakes Water Authority's Water Reclamation Plant (GLWA's WRP).

Methods

The comprehensive CD wastewater surveillance ranking system (CDWSRank) was developed based on 6 binary and 6 quantitative parameters. The final ranking scores of CDs were computed by summing the multiplication products of weighting factors for each parameter, and then were sorted based on decreasing priority. Disease incidence data from 2014 to 2021 were collected for the TCDA. Disease incidence trends in the TCDA were endowed with higher weights, prioritizing the TCDA over the state of Michigan.

Results

Disparities in incidences of CDs were identified between the TCDA and state of Michigan, indicating epidemiological differences. Among 96 ranked CDs, some top ranked CDs did not present relatively high incidences but were prioritized, suggesting that such CDs require significant attention by wastewater surveillance practitioners, despite their relatively low incidences in the geographic area of interest. Appropriate wastewater sample concentration methods are summarized for the application of wastewater surveillance as per viral, bacterial, parasitic, and fungal pathogens.

Discussion

The CDWSRank system is one of the first of its kind to provide an empirical approach to prioritize CDs for wastewater surveillance, specifically in geographies served by centralized wastewater collection in the area of interest. The CDWSRank system provides a methodological tool and critical information that can help public health officials and policymakers allocate resources. It can be used to prioritize disease surveillance efforts and ensure that public health interventions are targeted at the most potentially urgent threats. The CDWSRank system can be easily adopted to geographical locations beyond the TCDA.

Back-estimation of norovirus infections through wastewater-based epidemiology: A systematic review and parameter sensitivity

The amount of norovirus RNA (Ribonucleic Acid) in raw wastewater, collected from a wastewater treatment plant (WWTP), can provide an indication of disease prevalence within the sampled catchment. However, an accurate back-estimation might be impeded by the uncertainties from in-sewer/in-sample degradation of viral RNA, variable shedding magnitude, and difficulties in measurement within raw wastewater. The current study reviewed the published literature regarding the factors of norovirus shedding, viral RNA decay in wastewater, and the occurrence of norovirus RNA in raw wastewater based on molecular detection. Sensitivity analysis for WBE back-estimation was conducted using the reported data of the factors mentioned above considering different viral loads in wastewater samples. It was found that the back-estimation is more sensitive to analytical detection uncertainty than shedding variability for norovirus. Although seasonal temperature change can lead to variation of decay rates and may influence the sensitivity of this pathogen-specific parameter, decay rates of norovirus RNA contribute negligibly to the variance in estimating disease prevalence, based on the available data from decay experiments in bulk wastewater under different temperatures. However, the effects of in-sewer transportation on viral RNA decay and retardation by sewer biofilms on pipe surfaces are largely unknown. Given the highest uncertainty from analytical measurement by molecular methods and complexity of in-sewer processes that norovirus experienced during the transportation to WWTP, future investigations are encouraged to improve the accuracy of viral RNA detection in wastewater and delineate viral retardation/interactions with wastewater biofilms in real sewers.