Wastewater-based Epidemiology Research Articles

Harnessing Non-standard Nucleic Acids for Highly Sensitive Icosaplex (20-Plex) Detection of Microbial Threats for Environmental Surveillance.

Environmental surveillance and clinical diagnostics heavily rely on the polymerase chain reaction (PCR) for target detection. A growing list of microbial threats warrants new PCR-based detection methods that are highly sensitive, specific, and multiplexable. Here, we introduce a PCR-based icosaplex (20-plex) assay for detecting 18 enteropathogen and two antimicrobial resistance genes. This multiplexed PCR assay leverages the self-avoiding molecular recognition system (SAMRS) to avoid primer dimer formation, the artificially expanded genetic information system (AEGIS) for amplification specificity, and next-generation sequencing for amplicon identification. Using parallelized multitarget TaqMan Array Cards (TAC) to benchmark performance of the 20-plex assay on wastewater, soil, and human stool samples, we found 90% agreement on positive calls and 89% agreement on negative calls. Additionally, we show how long-read and short-read sequencing information from the 20-plex can be used to further classify allelic variants of genes and distinguish subspecies. The strategy presented offers sensitive, affordable, and robust multiplex detection that can be used to support efforts in wastewater-based epidemiology, environmental monitoring, and human/animal diagnostics.

Evaluation of plasmid pBI143 for its optimal concentration methods, seasonal impact, and potential as a normalization parameter in wastewater-based epidemiology.

Plasmid pBI143, abundant in the human gut, is a promising human-specific fecal marker. However, studies on its optimal concentration methods, seasonal variations, and potential as a normalization parameter for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), remain limited. Among the three concentration methods compared, polyethylene glycol (PEG) precipitation and centrifugation demonstrated comparable efficiencies (9.3±0.6 and 9.2±0.6 log10 copies/L, respectively; n=8 each), outperforming membrane filtration (8.0±0.6 log10 copies/L; n=8). PEG precipitation was further applied to quantify pBI143, together with other human-specific fecal markers (crAssphage and pepper mild mottle virus (PMMoV)), in 52 wastewater samples collected weekly over a one year from a wastewater treatment plant in Yamanashi Prefecture, Japan, by quantitative polymerase chain reaction. The higher pBI143 concentrations (9.6±0.5 log10 copies/L) compared to PMMoV (8.2±0.2 log10 copies/L) and crAssphage (8.0±0.2 log10 copies/L) highlighted its potential as a robust marker for human fecal contamination. Unlike PMMoV and crAssphage that remained stable across seasons, pBI143 showed seasonal fluctuations, especially during summer and autumn, suggesting its greater sensitivity to environmental conditions. The study evaluated the suitability of pBI143, crAssphage, and PMMoV for normalizing SARS-CoV-2 concentrations in wastewater; however, non-normalized SARS-CoV-2 concentrations showed the highest correlation with COVID-19 cases (ρ=0.74), whereas normalization reduced this correlation (PMMoV-normalized, ρ=0.72; crAssphage-normalized, ρ=0.70; and pBI143-normalized, ρ=0.50), likely due to differences in the persistence and structural properties of the markers, indicating that these markers are less effective for SARS-CoV-2 normalization. This study underscores the promising utility of pBI143 in wastewater surveillance but highlights the need for further research across diverse regions to validate its applicability.

P-1301. Pilot Study Utilizing Wastewater-Based Epidemiology to Determine Presence of Gram-Negative Enterobacterial Targets in Southwest Virginia Sewersheds

Abstract Background Wastewater-based epidemiology (WBE) detects and mitigates pathogen outbreaks by evaluating conditions in a specific community. Between January 2019 and September 2022, Escherichia coli, Citrobacter freundii, Pseudomonas aeruginosa, and Salmonella enterica comprised about one-third of the local health department’s reported healthcare-associated infections. This pilot study attempted to detect and quantitatively assess the prevalence of these bacterial pathogens in order to report public health data to the state health department, enabling effective outbreak preparedness and response. Pathogen Standard Curves with Plotted Unknowns Depicted above are the standard curves with plotted unknowns from the 12 weeks of collection (n=83) for each organism. Each linear equation and R2 value is represented for the standards. A: Salmonella spp. standards and unknowns. B: E. coli standards and unknowns. C: Pseudomonas spp. standards and unknowns. D: Citrobacter spp. standards and unknowns. Methods Wastewater samples (n=83, 100 mL) were collected from eight community watersheds in Roanoke and Salem, VA, including two healthcare facilities, over a 12-week period (February-May, 2023). Microbial total nucleic acids (TNAs) were extracted with the Promega Wizard enviro kit and quantified using a Nanodrop spectrophotometer. Bacterial DNA target detection was performed using gene-specific primers (Salmonella spp.: invA, E. coli: gadA; Pseudomonas spp.: 23S rRNA; Citrobacter spp.: tpl). TaqMan probe sets with unique fluorophores were selected with CFX Opus 96, and 16S rDNA was normalized with microbial population. Amplification efficiency was measured by using cycle quantification (Cq) value. Pathogen Cq and Copies/L Averages Each organism is depicted as follows according to color: purple, Salmonella spp.; red, E. coli; green, Pseudomonas spp.; and pink, Citrobacter spp.. A: Copies/L per organism for each location over 12 weeks. Average included for all locations. B: Cq per organism for each location over 12 weeks. Results E. coli (average of Cq 26.6), Citrobacter spp. (Cq 28.3), and Pseudomonas spp. (Cq 20.8) were detected in all locations and not significantly different with copies/L. Pseudomonas spp. had the lowest Cq in most of the wastewater samples (Figure 1). Salmonella spp. (Cq 44.1) was identified sporadically in locations, such as hospital and influent collections (1.52 x 1011) copies/L, which is significantly different at (P < 0.001)(Figure 2). 16S rDNA was detected in most samples (Cq 16.3)(Figure 3). Relative Abundance (%) of Microbial Population Relative abundance (%) of each organism is depicted by parenthesis. P-Values are represented as follows: *P < 0.05; **P < 0.1, ***P < 0.001. Conclusion According to the CDC, WBE in conjunction with other data collection can provide real-time trends of a population to promote public health. WBE surveillance targeting the most prevalent pathogens in communities can be a vital tier to public health defense. Reported findings indicate the presence of prevalent bacterial pathogens in the local Roanoke and Salem, VA sewersheds. Disclosures All Authors: No reported disclosures

Unsupervised detection of novel SARS-CoV-2 mutations and lineages in wastewater samples using long-read sequencing

The COVID-19 pandemic has underscored the importance of virus surveillance in public health and wastewater-based epidemiology (WBE) has emerged as a non-invasive, cost-effective method for monitoring SARS-CoV-2 and its variants at the community level. Unfortunately, current variant surveillance methods depend heavily on updated genomic databases with data derived from clinical samples, which can become less sensitive and representative as clinical testing and sequencing efforts decline.In this paper, we introduce HERCULES (High-throughput Epidemiological Reconstruction and Clustering for Uncovering Lineages from Environmental SARS-CoV-2), an unsupervised method that uses long-read sequencing of a single 1 Kb fragment of the Spike gene. HERCULES identifies and quantifies mutations and lineages without requiring database-guided deconvolution, enhancing the detection of novel variants.We evaluated HERCULES on Norwegian wastewater samples collected from July 2022 to October 2023 as part of a national pilot on WBE of SARS-CoV-2. Strong correlations were observed between wastewater and clinical sample data in terms of prevalence of mutations and lineages. Furthermore, we found that SARS-CoV-2 trends in wastewater samples were identified one week earlier than in clinical data.Our results demonstrate HERCULES’ capability to identify new lineages before their detection in clinical samples, providing early warnings of potential outbreaks. The methodology described in this paper is easily adaptable to other pathogens, offering a versatile tool for environmental surveillance of new emerging pathogens.

P-492. Quantification of HIV Shedding in Viremic Patients

Abstract Background Identifying pockets of undiagnosed HIV is a critical step in the U.S. Ending the HIV Epidemic (EHE). Wastewater-based epidemiology represents a novel way to build agnostic population-level data about disease distribution. We detected HIV virus in two sewersheds in Northern California. We are now conducting a study to measure how much HIV virus is shed into urine and stool, to better estimate cases represented by wastewater detection. HIV-1 RNA Detected in Concentrated Urine Methods We are recruiting participants from HIV clinics in San Mateo and Santa Clara Counties. Patients must be 18 years or older with HIV-1 viral load of >10,000 copies/ml at the time of recruitment. Subjects are asked to provide 5 weekly self-collected blood (Tasso), urine, and stool samples. The plasma is analyzed by established HIV-1 RNA quantitative PCR techniques. Nucleic acids from urine, concentrated (centrifuged) urine, and stool are suspended in DNA/RNA Shield, then extracted using the ZymoBIOMICS DNA/RNA Miniprep Kit (ZymoBIOMICS #R2002). HIV genome-specific primers target the Long Terminal Repeat (LTR) region of the HIV-1 genome. Droplets for digital droplet PCR are generated using the AutoDG Automated Droplet Generator. Reverse transcriptase PCR, which quantifies RNA and DNA combined, and direct PCR, which quantifies DNA, is then carried out with the Mastercycler Pro. Thresholding is performed using QX Manager Software. HIV-1 RNA Detected in Stool Results Two patients have been enrolled to date. Patient 1 was started on antiretroviral therapy after the first sample was obtained; Patient 2 was started on antiretroviral therapy 10 days before the first sample was collected. HIV-1 RNA was found in 87.5% (7/8) of concentrated urine samples, including 85.7% (6/7) of samples obtained while on ART (Table 1). Among stool samples, 25.0% (2/8) yielded HIV RNA, but only 14.3% (1/7) of those obtained on ART (Table 2). Nucleic acids were not detected in unconcentrated urine. Plasma viral load testing is pending, so only the initial clinical HIV-1 viral loads at enrollment are shown. Conclusion HIV-1 RNA is present in concentrated urine but also in stool samples of viremic patients, even up to five weeks after initiation of ART. When the study is complete, our results will bolster how to interpret HIV-1 detected in wastewater. Disclosures All Authors: No reported disclosures

Community case study: an academia-industry-government partnership that monitors and predicts outbreaks in Tri-County Detroit area since 2017

The Tri-County Detroit Area (TCDA) is the 12th most populous metropolitan area in the United States with over three million people. Multiple communicable diseases are endemic in the TCDA. In 2017, to explore innovative methods that may provide early warnings of outbreaks affecting populations in the TCDA, an exploratory partnership that was funded by a U.S. National Science Foundation Early-concept Grant for Exploratory Research (EAGER) began. Since 2017, a project team including the College of Engineering at Michigan State University (MSU), the City of Detroit, the Great Lakes Water Authority (GLWA), industry, and local government and health departments, has been testing municipal wastewater from the TCDA to survey and predict surges in communicable diseases in the area. This ongoing effort started years before wastewater-based epidemiology became a widespread method in public health practice, due to the COVID-19 pandemic, and is now supported by the U.S. Centers for Disease Control and Prevention (CDC). The work of the partnership led to significant breakthroughs in the field of wastewater surveillance/wastewater epidemiology. The results of our surveillance efforts are used to assist local health departments in their understanding and response efforts for health issues in the TCDA, facilitating public health messaging for local awareness, targeted clinical testing, and increased vaccination efforts. Our data are available to the local health departments, and our methodological advancements are published and have been used by other communities nationwide and beyond. This paper describes the partnership, lessons learned, significant achievements, and provides a look into the future. The successful implementations and advancements of wastewater surveillance in the TCDA advocate the importance of frequent communications and interactions within the partnership, idea generations from each stakeholder for decision-making, maintenance of scientific rigor, ethical awareness, and more.

Relationship between antihistamine residues in wastewater and airborne pollen concentrations: Insights into population-scale pollinosis response.

Pollinosis is the most prevalent allergic disorder. Assessing the impact of real-world pollen exposure on symptoms remains challenging due to extensive patient-level efforts required. This study explores the potential of wastewater-based epidemiology (WBE) to investigate the relationship between airborne pollen concentrations and antihistamine residues in wastewater as an indicator of pollinosis symptom treatment at the population-scale. In Zurich (Switzerland), 279 wastewater samples were collected during 2021-2023. Each sample represents a 24-h period with excreta from approximately 471,000 individuals. Eleven antihistamine markers were analyzed in the samples using liquid chromatography high-resolution mass spectrometry. The relationship between antihistamine loads in wastewater and airborne pollen concentrations (47 taxa and miscellaneous pollen) was investigated by determining seasonal components of antihistamine loads and exploring pollen-specific contributions using Non-Negative Least Squares (NNLS) optimization. Four antihistamines were detected in quantifiable amounts in wastewater. The first-generation antihistamine, diphenhydramine, demonstrated rather constant levels. In contrast, the three second-generation antihistamines - bilastine, cetirizine, and fexofenadine - showed pronounced day-to-day variation with strong correlations among each other. For fexofenadine, which was investigated in detail for its correlation with airborne pollen concentrations, approximately 50% of the annual wastewater loads were associated with acute pollen exposure. Another 20% related to baseline consumption during the pollen season, while the remaining 30% seems unrelated to pollen. Birch, grasses, hazel, hornbeam, plane, and plantain explained most of the variance in wastewater loads (R2=0.82), with grass pollen alone accounting for a quarter of the annual loads. Increased fexofenadine loads during periods without elevated concentrations of common allergenic pollen suggests the presence of additional triggers for allergy symptoms, potentially yew pollen. Our study demonstrates that WBE can effectively reveal substantial day-to-day variation in antihistamine use related to pollen exposure. Thus, WBE presents an objective and questionnaire-independent method for investigating pollinosis symptom treatment at a population-scale.

A Perspective on Wastewater and Environmental Surveillance as a Public Health Tool for Low- and Middle-Income Countries

Geographical variations in infectious diseases create differences in public health priorities between high- and low-income countries. Low- and middle-income countries (LMICs) face resource constraints that limit adherence to international monitoring standards for wastewater-based epidemiology (WBE). The development of low-cost WBE programs, such as those to detect SARS-CoV-2, offers LMICs a promising tool for monitoring pathogens of local concern. In this work, we summarize important wastewater biomarkers for LMICs and their associated public health challenges, ranging from pathogens causing gastroenteritis to putative markers for plant diseases linked to food safety, as well as antimicrobial resistance. We raise awareness of the great potential of WBE for LMICs and highlight the critical health markers, research needs, and strategies necessary to establish tailored wastewater surveillance programs.

Wastewater Surveillance for SARS-CoV-2 in Northern Italy: An Evaluation of Three Different Gene Targets

Wastewater-based epidemiology has emerged as a complementary tool for the monitoring of COVID-19 pandemic waves and for the circulation of viral variants. The selection, standardization, and dynamics of different SARS-CoV-2 RNA targets in wastewater requires further investigation. In the present study, 106 wastewater samples were collected over a 24-month period from the wastewater treatment plant of Sondrio, north Italy, and were analyzed for the presence of SARS-CoV-2 RNA through the quantification of ORF1b, N1, and N3 gene targets via one-step real-time qPCR. In general, the three RNA targets demonstrated different performances and dynamics over the studied time period, underlying the usefulness of multiple viral targets in the surveillance of SARS-CoV-2 in wastewater. During the first 12 months, the quantification of the selected SARS-CoV-2 viral targets also correlated with the reported clinical cases in the same geographical area; however, from the overall data analysis this did not appear to significantly anticipate the epidemic waves. In conclusion, this study further supports the use of wastewater surveillance as a real time indicator of the human circulation of SARS-CoV-2. Moreover, the use of multiple viral gene targets has been shown to improve the reliability of SARS-CoV-2 surveillance in wastewater over time.

Eight Years of Norovirus Surveillance in Urban Wastewater: Insights from Next-Generation.

Human noroviruses (HNoVs) are a leading cause of acute gastroenteritis worldwide, with significant public health implications. In this study, wastewater-based epidemiology (WBE) was used to monitor the circulation and genetic diversity of HNoVs in Rome over an eight-year period (2017-2024). A total of 337 wastewater samples were analyzed using RT-nested PCR and next-generation sequencing (NGS) to identify genogroups GI and GII and their respective genotypes. The results showed that GII had higher detection rates (66.5%) compared to GI (50.7%), with significant variation between years. Detection rates peaked in 2019 before declining sharply in 2020, coinciding with the COVID-19 pandemic and rebounding after the pandemic in 2023. A total of 24 genotypes were identified (8 GI and 17 GII), including persistent variants GII.2, GII.3 and GII.4 and emerging genotypes such as GII.8, GII.10 and GII.14. Only two GII.4 variants, Sydney_2016 and Sydney_2012, were detected in the study. These results demonstrate the utility of WBE in tracking HNoVs circulation, identifying genotype diversity and capturing shifts in transmission dynamics. WBE provides a cost-effective and comprehensive tool for public health surveillance, particularly in regions with limited clinical surveillance. Sustained investment in WBE is crucial for advancing our understanding of HNoVs epidemiology and its long-term trends.

Wastewater Monitoring During the COVID-19 Pandemic in the Veneto Region, Italy: Longitudinal Observational Study.

As the COVID-19 pandemic has affected populations around the world, there has been substantial interest in wastewater-based epidemiology (WBE) as a tool to monitor the spread of SARS-CoV-2. This study investigates the use of WBE to anticipate COVID-19 trends by analyzing the correlation between viral RNA concentrations in wastewater and reported COVID-19 cases in the Veneto region of Italy. We aimed to evaluate the effectiveness of the cumulative sum (CUSUM) control chart method in detecting changes in SARS-CoV-2 concentrations in wastewater and its potential as an early warning system for COVID-19 outbreaks. Additionally, we aimed to validate these findings over different time periods to ensure robustness. This study analyzed the temporal correlation between SARS-CoV-2 RNA concentrations in wastewater and COVID-19 clinical outcomes, including confirmed cases, hospitalizations, and intensive care unit (ICU) admissions, from October 2021 to August 2022 in the Veneto region, Italy. Wastewater samples were collected weekly from 10 wastewater treatment plants and analyzed using a reverse transcription-quantitative polymerase chain reaction. The CUSUM method was used to detect significant shifts in the data, with an initial analysis conducted from October 2021 to February 2022, followed by validation in a second period from February 2022 to August 2022. The study found that peaks in SARS-CoV-2 RNA concentrations in wastewater consistently preceded peaks in reported COVID-19 cases by 5.2 days. Hospitalizations followed with a delay of 4.25 days, while ICU admissions exhibited a lead time of approximately 6 days. Notably, certain health care districts exhibited stronger correlations, with notable values in wastewater anticipating ICU admissions by an average of 13.5 and 9.5 days in 2 specific districts. The CUSUM charts effectively identified early changes in viral load, indicating potential outbreaks before clinical cases increased. Validation during the second period confirmed the consistency of these findings, reinforcing the robustness of the CUSUM method in this context. WBE, combined with the CUSUM method, offers valuable insight into the level of COVID-19 outbreaks in a community, including asymptomatic cases, thus acting as a precious early warning tool for infectious disease outbreaks with pandemic potential.

Evaluating small extracellular vesicle-based vaccination across heterologous Salmonella strains isolated from wastewater.

Salmonella infections pose significant public health challenges worldwide. The diversity of Salmonella strains, particularly those isolated from environmental and clinical sources, necessitates innovative approaches to prevention and treatment. Previous research has shown that small extracellular vesicles (sEVs) produced by macrophages during Salmonella Typhimurium infection can induce robust immune responses when used as a vaccine, offering complete protection in systemic infection models. In this study, we isolated 120 Salmonella strains from qPCR invA-positive wastewater samples collected in Gainesville, FL. These strains underwent enrichment, selection, and biochemical confirmation, followed by serotyping and whole genome sequencing. Two isolates, Salmonella enterica subsp. diarizonae (Diarizonae) and S. enterica serovar Enteritidis, were selected for further analysis based on community prevalence and clinical severity. We also assessed the ability of sEVs produced by S. Typhimurium-infected macrophages to induce immune responses against these heterologous and circulating strains in mice. Immunization with sEVs induced robust antigen-specific SIgA and IgG responses against S. Typhimurium, Enteritidis, and Diarizonae, with high titers observed in sera and fecal samples. Proteomic analysis revealed differential expression of proteins in these strains, including antigenic proteins present in sEVs such as OmpA, FliC, or OmpD. Moreover, this study highlights the role of wastewater-based epidemiology (WBE) as a tool for environmental surveillance, offering a complementary perspective on Salmonella dynamics within a population. Integrating WBE with traditional surveillance methods, along with the promising results of sEV-based vaccination, provides a pragmatic strategy for developing effective preventative measures against Salmonella infections, addressing the diversity of non-typhoidal Salmonella strains.

Metagenomics unveils the role of hospitals and wastewater treatment plants on the environmental burden of antibiotic resistance genes and opportunistic pathogens.

Antimicrobial resistance (AMR) is a global health challenge, with hospitals and wastewater treatment plants (WWTPs) serving as significant pathways for the dissemination of antibiotic resistance genes (ARGs). This study investigates the potential of wastewater-based epidemiology (WBE) as an early warning system for assessing the burden of AMR at the population level. In this comprehensive year-long study, effluent was collected weekly from three large hospitals, and treated and untreated wastewater were collected monthly from three associated community WWTPs. Metagenomic analysis revealed a significantly higher relative abundance and diversity of ARGs in hospital wastewater than in WWTPs. Notably, ARGs conferring resistance to clinically significant antibiotics such as β-lactams, aminoglycosides, sulfonamides, and tetracyclines were more prevalent in hospital effluents. Conversely, resistance genes associated with rifampicin and MLS (macrolides-lincosamide-streptogramin) were more commonly detected in the WWTPs, particularly in the treated effluent. Network analysis identified the potential bacterial hosts, which are the key carriers of these ARGs. The study further highlighted the variability in ARG removal efficiencies across the WWTPs, with none achieving complete elimination of ARGs or a significant reduction in bacterial diversity. Additionally, ARG profiles remained relatively consistent in hospital and community wastewater throughout the study, indicating a persistent release of a baseload of ARGs and pathogenic bacteria into surface waters, potentially polluting aquatic environments and entering the food chain. The study underscores the need for routine WBE surveillance, enhanced wastewater treatment strategies, and hospital-level source control measures to mitigate AMR dissemination into the environment.

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 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.

Targeted Enrichment Sequencing Utilizing a Respiratory Pathogen Panel for Genomic Wastewater-Based Viral Epidemiology in Uruguay.

Human respiratory and enteric viruses are responsible for substantial morbidity and mortality worldwide. Wastewater-based epidemiology utilizing next-generation sequencing serves as an effective tool for monitoring viral circulation dynamics at the community level. However, these complex environmental samples are often laden with other microorganisms and host genomic material, which can hinder the sensitivity of viral detection. To address this limitation, targeted enrichment sequencing is emerging as a preferred strategy, facilitating the acquisition of a more comprehensive understanding of specific pathogens. In this study, we evaluated the performance of a targeted enrichment sequencing panel for 42 excreted respiratory viruses (including Picornaviridae, Adenoviridae, Coronaviridae, Paramyxoviridae, Orthomyxoviridae, Orthoherpesviridae, Pneumoviridae, and Parvoviridae families), known as the Respiratory Pathogen ID/AMR enrichment panel (RPIP), coupled with Explify bioinformatics analysis in 3 sewage samples from Uruguay. RPIP panel successfully identified sequences from frequently circulating viruses, along with some that had not been documented previously. We identified and characterized various viruses, including human Enterovirus (Coxsackievirus A1 and A19), Influenza A-H1N1, and full-length sequences of SARS-CoV-2. Additionally, several other viral pathogens were detected, such as human Bocavirus, human Parechovirus, Enterovirus A71, and Enterovirus D68; however, for these viruses further analysis was limited due to the small genomic regions or low-read coverage obtained. While the RPIP panel necessitates substantial sequencing depth and may introduce bias towards the more predominant strains present in the samples, this approach suggests its viability as a genomic epidemiological tool for assessing respiratory and enteric viruses in wastewater.

Development of Bayesian segmented Poisson regression model to forecast COVID-19 dynamics based on wastewater data: a case study in Nanning City, China

IntroductionCOVID-19 has caused tremendous hardships and challenges around the globe. Due to the prevalence of asymptomatic and pre-symptomatic carriers, relying solely on disease testing to screen for infections is not entirely reliable, which may affect the accuracy of predictions about the pandemic trends. This study is dedicated to developing a predictive model aimed at estimating of the dynamics of COVID-19 at an early stage based on wastewater data, to assist in establishing an effective early warning system for disease control.MethodViral load in wastewater and the number of daily reported COVID-19 cases were collected from Nanning CDC and the Chinese Disease Prevention and Control Information System, respectively. We used the viral load to estimate daily reported cases by a Bayesian linear regression model. Subsequently, a Bayesian (segmented) Poisson regression model was developed, using data from the first wave of the epidemic as prior information, to predict the COVID-19 epidemic trend of the second wave. Finally, in order to explore the optimal training data for predicting outbreak dynamics during the pandemic, we fitted the model using various training sets.ResultsThe results revealed the estimated cases, using the viral load with a 3-day lag, were consistent with the actual reported cases, with adjusted R² value of 0.935 (p < 0.001). Our model successfully predicted the epidemic peak time and provided early warnings on the third day after the outbreak began. Furthermore, after using data from the first 6 days of the outbreak, the model’s MAPE rapidly decreasing to lower levels (MAPE = 29.34%) and eventually stabilized at approximately 20%. Compared to using non-informative priors, this result allows for an advance warning of approximately two weeks. Importantly, as the inclusion of data from early outbreak increased, the predictive results of the model became more stable and accurate.ConclusionThis study demonstrates the potential of wastewater-based epidemiology combined with Bayesian methods as a monitoring and predictive tool during infectious disease outbreaks.

Efficacy of cleaning, disinfection, and sterilization modalities for addressing infectious drug-resistant fungi: a review.

This is a timely and important review that focuses on the appropriateness of established cleaning, disinfection, and sterilization methods to safely and effectively address infectious fungal drug-resistant pathogens that can potentially contaminate reusable medical devices used in healthcare environment in order to mitigate the risk of patient infection. The release of the World Health Organization (WHO) fungal priority pathogen list (FPPL) in 2022 highlighted the public health crisis of antimicrobial resistance (AMR) in clinically relevant fungal species. Contamination of medical devices with drug-resistant fungal pathogens (including those on the FPPL) in healthcare is a rare event that is more likely to occur due to cross-transmission arising from lapses in hand hygiene practices. Established disinfection and sterilization methods decontaminate fungal pathogens on single-use and reusable medical devices; however, there are assumptions that reusable devices destined for semi-critical use are appropriately cleaned and do not harbour biofilms that may undermine the ability to effectively decontamination these type devices in healthcare. International standards dictate that manufacturer's instructions for use must provide appropriate guidance to healthcare facilities to meet safe reprocessing expectations that include addressing drug-resistant fungal pathogens. Increased environmental monitoring and vigilance surrounding fungal pathogens in healthcare is advised, including adherence to hand hygiene/aseptic practices and appropriate cleaning encompassing the simplification of reusable device features for 'ease-of-reach'. There are emereging opportunities to promote a more integrated multiactor hub approach to addressing these sophisticated challenges, including future use of artificial intelligence and machine learning for improved diagnostics, monitoring/surveillance (such as healthcare and wastewater-based epidemiology), sterility assurance, and device design. There is a knowledge gap surrounding the occurrence and potential persistence of drug-resistant fungal pathogens harboured in biofilms, particularly for ascertaining efficacy of high-level disinfection for semi-critical use devices.

A multivariate analysis to explain residue errors in pathogen concentration in wastewater-based epidemiology.

With the beginning of the COVID-19 pandemic, wastewater-based epidemiology (WBE), which according to Larsen et al. (2021), describes the science of linking pathogens and chemicals found in wastewater to population-level health, received an enormous boost worldwide. The basic procedure in WBE is to analyse pathogen concentrations and to relate these measurements to cases from clinical data. This prediction of cases is subject to large errors, due to various factors such as dilution effects, decay or wastewater matrix and inhibitors. In this study we used different models to identify the most important, what we call, wastewater-based epidemiologically relevant parameters (WBERP) to describe these errors. We used linear regression and random forest regression as base models for predicting cases and random forest regression also to analyse the importance of different WBERP. Two catchments, one with a large proportion of combined sewers and one with separate sewers, served as study areas. Our results show that the most important information to be included in any model are the variants of concern (VOCs), a time-variable parameter. The performance for both catchments is improved by ~30% in terms of root mean square error when the VOCs are used as additional information. For practical applications, this is a real drawback as it means that every time a new pathogen variant becomes dominant, we need to know the specific behaviour of the variant in the wastewater and its detection in order to interpret the WBE data correctly. This limits the predictive capabilities of such systems, perhaps not in terms of dynamics but for quantitative statements. The addition of other physicochemical parameters and faecal markers only marginally improved the results. Furthermore, there were differences in the importance of the parameters between the catchments, which limits the generalisability of the conclusions. The results show that more complex wastewater matrices (high proportion of combined sewer system) influence the relationship between pathogen concentration and medical cases more than those of less complex wastewater matrices (separate sewer system).

Lifting of travel restrictions brings additional noise in COVID-19 surveillance through wastewater-based epidemiology in post-pandemic period.

The post-pandemic world still faces ongoing COVID-19 infections, although international travel has returned to pre-pandemic conditions. Wastewater-based epidemiology (WBE) is considered an efficient tool for the population-wide surveillance of COVID-19 infections during the pandemic. However, the performance of WBE in post-pandemic era with travel restrictions lifted remains unknown. Utilizing weekly county-level wastewater surveillance data from June 2021-November 2022 for 222 counties in 49 states (covering 104 million people) in the United States of America, we retrospectively evaluated the correlations between SARS-CoV-2 RNA (CRNA) and reported cases, as well as the impacts of international air travel, demographics, socioeconomic aspects, test accessibility, epidemiological, and environmental factors on reported cases under the corresponding CRNA. The lifting of travel restrictions in June 2022, shifted the correlation between CRNA and COVID-19 incidence in the following 7-day and 14-day from 0.70 (IQR: 0.30-0.88) in June 2021-May 2022 (pandemic) to 0.01 (IQR: -0.31-0.36) in June-November 2022 (post-pandemic), and from 0.74 (IQR: 0.31-0.90) to -0.01 (IQR: -0.38-0.45), respectively. Besides, after lifting the travel restrictions, under the same CRNA, the reported case numbers were impacted by many factors, including the variations of international passengers, test accessibility, Omicron prevalence, ratio of population aged between 18 and 65, minority vulnerability, and healthcare system. This highlights the importance of demographics, infection testing, variants and socioeconomic status on the accuracy and implication of WBE to monitor COVID-19 infection status in post-pandemic era. Our findings facilitate the public health authorities to dynamically adjust their WBE-based tools/strategies to the local contexts to achieve optimal community surveillance.

A multi-city COVID-19 forecasting model utilizing wastewater-based epidemiology.

The COVID-19 pandemic highlighted shortcomings in forecasting models, such as unreliable inputs/outputs and poor performance at critical points. As COVID-19 remains a threat, it is imperative to improve current forecasting approaches by incorporating reliable data and alternative forecasting targets to better inform decision-makers. Wastewater-based epidemiology (WBE) has emerged as a viable method to track COVID-19 transmission, offering a more reliable metric than reported cases for forecasting critical outcomes like hospitalizations. Recognizing the natural alignment of wastewater systems with city structures, ideal for leveraging WBE data, this study introduces a multi-city, wastewater-based forecasting model to categorically predict COVID-19 hospitalizations. Using hospitalization and COVID-19 wastewater data for six US cities, accompanied by other epidemiological variables, we develop a Generalized Additive Model (GAM) to generate two categorization types. The Hospitaization Capacity Risk Categorization (HCR) predicts the burden on the healthcare system based on the number of available hospital beds in a city. The Hospitalization Rate Trend (HRT) Categorization predicts the trajectory of this burden based on the growth rate of COVID-19 hospitalizations. Using these categorical thresholds, we create probabilistic forecasts to retrospectively predict the risk and trend category of six cities over a 20-month period for 1, 2, and 3week forecasting windows. We also propose a new methodology to measure forecasting model performance at change points, or time periods where sudden changes in outbreak dynamics occurred. We also explore the influence of wastewater as a predictor for hospitalizations, showing its inclusion positively impacts the model's performance. With this categorical forecasting study, we are able to predict hospital capacity risk and disease trends in a novel and useful way, giving city decision-makers a new tool to predict COVID-19 hospitalizations.