ACS ES&T Water

Wastewater-based surveillance (WBS) is widely used to monitor respiratory viruses, yet uncertainties remain regarding how viral RNA concentrations in wastewater reflect infection dynamics. Specifically, diurnal variation in shedding and RNA losses during in-sewer transport can impact measured signals. We conducted a field study in a 5-km trunk sewer (travel time of one hour). Wastewater was sampled at the sewer inlet and outlet using autosamplers collecting time-proportional one-hour composite samples over 24 hours. The one-hour composite samples were analyzed for assessing intra-daily fluctuations, and 24-hour composites for signal change. Biofilms from the sewer-pipe walls were collected at three locations. Nucleic acids were extracted, and SARS-CoV-2, Influenza A/B, and Respiratory Syncytial Virus (RSV) RNA were quantified using a multiplex digital PCR assay. All viruses showed pronounced diurnal variation, with consistent morning load peaks. Viral RNA in the bulk liquid decreased during in-sewer transport, with modelled changes ranging from 15% to 72% across pathogens. Biofilms served as minor reservoirs of viral RNA; for SARS-CoV-2, sequencing revealed similarity between biofilm and bulk liquid RNA. Our study provides a full-scale assessment of in-sewer transport effects on viral RNA and highlights the need to account for complex in-sewer dynamics when interpreting WBS data.

IntroductionWastewater-based epidemiology (WBE) was implemented at the 2024 Republican and Democratic National Conventions (RNC and DNC, respectively)--two prominent large-scale events, each with estimated attendances of >50,000 persons. In preparation for event monitoring, the Wisconsin and Chicago WBE programs (associated with the RNC and DNC public health response, respectively) developed site-specific monitoring strategies and response plans, prioritized additional pathogens for event surveillance, and further optimized laboratory workflows to ensure rapid daily data reporting to public health. The Chicago program expanded the sewer sampling network to include new locations closer to event venues than previously available. Sampling was also conducted before the events, to establish baselines for endemic pathogens, as well as after each event to monitor for residual community transmission. MethodsSurveillance was expanded from the four respiratory pathogens regularly assessed by both WBE programs (SARS-CoV-2, influenza A, influenza B, respiratory syncytial virus) to include 3 gastrointestinal pathogens (norovirus, Salmonella enterica, Shiga toxin-producing E. coli). The Wisconsin program also conducted monitoring for the measles, mumps, rubella, and hepatitis A viruses. Wastewater sampling for the RNC was conducted at the community water reclamation facility level, while at the DNC samples were collected from manholes located downstream of the event venues. For both events, WBE data were summarized and contextualized alongside traditional public health surveillance data in daily situation reports. ResultsBetween the RNC and DNC response, a total of 112 wastewater samples were collected and assayed to provide concentration data on as many as 11 distinct pathogens of interest. Concentration results for the suite of pathogens were available within 12 to 36 hours of sample collection. In each instance when wastewater concentrations exceeded pre-established thresholds for action and flagged as an alert, other sources of contemporaneous public health surveillance information (e.g., clinical data) did not corroborate the WBE findings. ConclusionExisting WBE infrastructure in two U.S. cities was readily adapted for public health surveillance at two high-profile, large-scale events. Assays for additional event-relevant pathogens were quickly incorporated into routine laboratory workflows and data from wastewater samples were generated and reported with rapid turnaround-time. In considering the unique benefits of wastewater data, WBE results were a valuable supplement to other public health surveillance data in monitoring potential public health threats during these two large-scale events.

Infectious disease forecasts can inform public health decision-making. Wastewater monitoring is a relatively new epidemiological data source with multiple potential applications, including forecasting. Incorporating wastewater data into epidemiological forecasting models is challenging, and relatively few studies have assessed whether this improves forecast performance. We present and evaluate a semi-mechanistic wastewater-informed forecasting model. The model forecasts COVID-19 hospital admissions at the state and territorial levels in the United States, based on incident hospital admissions data and, optionally, SARS-CoV-2 wastewater concentration data from multiple wastewater sampling sites. From February through April 2024, we produced real-time wastewater-informed COVID-19 forecasts using development versions of the model and submitted them to the United States COVID-19 Forecast Hub ("the Hub"). We then published an open-source R package, wwinference, that implements the model with or without wastewater as an input. Using proper scoring rules and measures of model calibration, we assess both our real-time submissions to the Hub and retrospective hypothetical forecasts from wwinference made with and without wastewater data. While the models performed similarly with and without the wastewater signal included, there was substantial heterogeneity for individual locations and dates where wastewater data meaningfully improved or degraded the models forecast performance. Compared to other models submitted to the Hub during the period spanned by our submissions, the real-time wastewater-informed version of our model ranked fourth of 10 models, with the hospital admissions-only version of our model ranking second out of 10 models. Across the 2023-2024 winter epidemic wave, retrospective forecasts from wwinference would have performed similarly with and without the wastewater signal included: fifth and fourth out of 10 models, respectively. To better understand the drivers of differential forecast performance with and without wastewater, we performed an exploratory analysis investigating the relationship between characteristics of the input data and improved and reduced performance in our model. Based on that analysis, we identify and discuss key areas for further model development. To our knowledge, this is the first work that conducts an evaluation of real-time and retrospective infectious disease forecasts across the United States both with and without wastewater data and compared to other forecasting models. Author SummaryWastewater-based epidemiology, in combination with clinical surveillance, has the potential to improve situational awareness and inform outbreak responses. We developed a model that uses data on the pathogen concentration in wastewater from one or more wastewater treatment plants in combination with hospital admissions to produce short-term forecasts of hospital admissions. We produced and submitted forecasts of 28-day ahead COVID-19 hospital admissions from this model to the U.S. COVID-19 Forecast Hub during the spring of 2024 and found that it performed well in comparison to other models during that limited time period. To assess the added value of incorporating wastewater data into the model and to investigate how it would have performed had we submitted it during the entire 2023-2024 winter epidemic wave, we performed a retrospective analysis in which we produced forecasts from the model with and without including wastewater data, using data that would have been available in real-time as of each forecast date. Both versions of the model would have been median overall performers had they been submitted to the Hub throughout the season. When comparing the models performance with and without wastewater data included, we found that overall forecast performance was very similar, with wastewater data slightly reducing overall average forecast performance. Within this result, there was significant heterogeneity, with clear instances of wastewater data improving and detracting from forecast performance. We used trends in the observed data to generate hypotheses as to the drivers of improved and reduced relative forecast performance within our model. We conclude by suggesting future work to improve the model and more broadly the application of wastewater-based epidemiology to forecasting.

Digital PCR (dPCR) is increasingly used for SARS-CoV-2 wastewater surveillance due to its precision, absolute quantification, and reduced sensitivity to inhibition compared to quantitative PCR. Although the Bio-Rad ddPCR and QIAGEN QIAcuity dPCR platforms are widely adopted, their performance has not been directly compared for wastewater applications. We conducted a blinded comparison of these platforms using 95 archived wastewater influent samples from North Carolina collected in 2021-2022, spanning three orders of magnitude in SARS-CoV-2 concentration (1x103 to 5x105 copies L-1). Samples were stratified into low, medium, and high concentration bins and analyzed in triplicate for N1 and N2 gene targets and a bovine coronavirus processing control. Both platforms demonstrated statistically equivalent quantification across all targets, with mean differences [≤]0.12 log copies L-1 (R2 > 0.93). Coefficients of variation were similar (3.96 - 7.61%), with no significant differences across concentration bins except for N2 in the low bin (difference: 0.87 percentage points). Measurement variability correlated strongly with wastewater treatment plant site (R2 = 0.89) rather than platform, indicating that sample matrix characteristics drive precision more than analytical platform. Process limits of detection ranged from 2,160-2,680 copies L-1 for Bio-Rad and 5,650-9,700 copies L-1 for QIAcuity for N1 and N2, respectively. The Bio-Rad platform processed samples 32% faster (305 vs. 435 minutes per 96 wells), while QIAcuity offered 29% lower consumables cost ($4.68 vs. $6.11 per well). These findings support the interchangeable use of both platforms for wastewater surveillance, with platform selection based on laboratory-specific operational needs. ImportanceAs wastewater-based epidemiology transitions from emergency response to sustained public health infrastructure, standardized molecular methods are essential for reliable data integration across surveillance networks. This study provides the first blinded comparison of two digital PCR platforms widely deployed for wastewater pathogen surveillance in the United States. We demonstrate quantitative equivalence between Bio-Rad ddPCR and QIAGEN QIAcuity platforms across three orders of magnitude in viral concentration, establishing that data from both platforms can be interpreted interchangeably for public health decision-making. This platform equivalence is critical as national surveillance systems aggregate data from diverse laboratories and as monitoring expands beyond SARS-CoV-2 to encompass additional respiratory viruses, antimicrobial resistance genes, and emerging pathogens. Our findings provide a methodological foundation for multi-platform surveillance networks and demonstrate that measurement variability is driven primarily by sample matrix characteristics rather than analytical platform choice.

Wastewater based epidemiology offers a valuable population level signal for monitoring respiratory virus activity, but its routine use in public health practice requires alerting methods that are transparent, interpretable, and comparable across locations. In this study, we propose a simple early warning framework that transforms wastewater viral RNA measurements into actionable alerts using a standardized statistical process control approach. The method relies on variance stabilization, site specific baseline normalization, and an exponentially weighted moving average to identify sustained increases in viral activity. To support operational relevance, wastewater derived alerts are benchmarked against established laboratory surveillance systems using a harmonized onset definition. The proposed framework emphasizes clarity, auditability and adaptability rather than complex forecasting, enabling straightforward interpretation by public health practitioners. Our results demonstrate that wastewater signals can provide timely situational awareness for respiratory virus circulation and support their use as a complementary tool for public health surveillance and preparedness.

Indigenous, mountain communities residing upstream of Bhumibol Dam, Thailand, rely on vulnerable natural water sources for their water supply, yet remain unaware of the associated health risks. This study assessed the water quality, usage patterns and contamination pathways across six villages upstream of Bhumibol Dam to shed light on the obstacles to sustainable water security . Samples from 38 water sources of drinking and/or non-drinking water, soil, and the edible parts of crops were subjected to analyses of physical, chemical (NO3-N, pH), and qualitative pesticide-related variables, alongside a 6-month assessment of a community water filter system. Principal component analysis identified a "at-risk group" of preferred drinking water sources all exhibiting high NO3-N, highly alkaline pH, and substantial pesticide contamination, which was found to likely be caused by agricultural run-off. This was reinforced by the detection of pesticide residues in all soil samples and, critically, in the below-ground edible parts of crops (taro, lemongrass, arrowroot), confirming dietary exposure in the local communities. Further compounding the risks posed by the unsafe water supply, the community water filter was found to be ineffective throughout the 6-month analysis with there being no significant difference in water quality between before and after filtration. The residents paradoxical preference for high-risk, still water (from sand-filtered puddles) for drinking, rather than water from flowing sources, which they used only for cooking and cleaning. These findings reveal a severe, compounded public health threat of chronic exposure to minerals linked to urolithiasis and agrochemicals, highlighting the urgent need for quantitative risk assessment and the implementation of resilient, decentralized water treatment solutions in these mountain communities.

Antimicrobial resistance (AMR) is a growing problem, with annual deaths set to pass 10 million by 2050 if current trends continue. Wastewater surveillance has been proposed as a strategy to understand population-level resistance, and water reclamation facilities (WRFs) have been identified as a control point for environmental dissemination of resistant bacteria. Understanding dynamics of AMR across WRFs requires advanced molecular tools that elucidate host bacteria, especially for mobile resistance carried on plasmids. To that end, influent, activated sludge, and effluent were collected from three WRFs in North Carolina, Texas, and California during three weeks of Spring 2024. Samples were analyzed using Hi-C proximity ligation sequencing to identify the AMR host range for chromosomal and plasmid-based resistance. A total of 1,868 hits for 244 unique resistance genes were observed, with seven resistance genes identified in all samples. Resistance genes were more likely to be carried on a microbial plasmid in influent, but more likely to be in a chromosome in activated sludge. Seventeen total microbial hosts for resistance genes were identified in effluent, suggesting WRF effluents may be sources of resistant bacteria to receiving surface waters. A high proportion of all identified host relationships were confined to just four bacterial families. Hi-C contact mapping is a critical tool to more fully describe the AMR host range in complex matrices, particularly for plasmid-based resistance genes. ImportanceAntimicrobial resistance (AMR) threatens modern medicine. Water reclamation facilities receive a complex mixture of antibiotics and rely on active microbial communities for treatment, thereby acting as critical systems to prevent environmental spread of resistance. However, AMR dynamics are difficult to discern in complex wastewater environments due to antibiotic resistance genes (ARGs) being frequently carried on mobile pieces of DNA that are difficult to link to specific bacteria using conventional shotgun sequencing. Novel proximity ligation sample preparation techniques like Hi-C physically link co-located sequences of DNA before shotgun sequencing. This allows sequencing to elucidate the bacterial hosts for both stable and mobile ARGs. In the current study, Hi-C sequencing was carried out on influent, activated sludge, and effluent collected from water reclamation facilities in California, Texas, and North Carolina to assess the resistome host range across treatment. 5 Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/26346186v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1e4620eorg.highwire.dtl.DTLVardef@e1c3a7org.highwire.dtl.DTLVardef@1f40964org.highwire.dtl.DTLVardef@94b886_HPS_FORMAT_FIGEXP M_FIG C_FIG

After the COVID-19 pandemic, wastewater monitoring is increasingly used for infectious disease surveillance. Using the data from a controlled experimental hospital setting, this paper examines the association wastewater SARS-CoV-2 with COVID-19 hospital admission and mortality, and whether this association varies by patients characteristics. Weekly wastewater samples were collected from the University of Miami (UM) hospitals where COVID-19 patients were admitted from February 2020 to October 2022, and SARS-CoV-2 was quantified using qPCR. Data on hospital admissions and their mortality and demographic characteristics and comorbidities were acquired from the UM hospitals. Using factor analysis and hierarchical clustering, patients were stratified into four clusters. Frist, we examined cross-correlations between time-lagged COVID-19 hospital admission and mortality, and time-lagged SARS-CoV-2 to identify appropriate time-lags. Second, we modelled daily hospital COVID-19 cases and mortality with respect to time-lagged SARS-CoV-2, vaccine status and time-lagged COVID-19 hospital cases (as proxy of the risk factor for the transmission of the disease for each cluster separately and for all clusters together. 1,856 COVID-19 patients were admitted in the UM hospitals during the study period and 347 (18.7%) of them died. In cluster 4 that represented patients with preexisting chronic health conditions and intubation, the fatality rate was 59%. COVID-19 hospital admission showed strong (temporal) autocorrelation, suggesting that the preexisting cases can indicate the transmission rate of infection. Our analysis suggests that a 1% increase in SARS-CoV-2 was associated with a 0.28% increase in COVID-19 related hospital admission ({beta} [~] 0.275; 95 % CI = 0.18 to 0.37; p < 0.01). Both a week lagged auto-regressive COVID-19 cases and SARS-CoV-2 in wastewater together explained 89% of the total variation in hospital admission due to COVID-19. Among four clusters, the second cluster of minority communities showed the strongest association between time-lagged SARS-CoV-2 in wastewater and hospital admissions due to COVID-19 followed by cluster 1 of adult patients with low prevalence of preexisting health conditions. However, time-lagged wastewater SARS-CoV-2 did not show any significant association with COVID-19 hospital admission for patients with the pre-existing health conditions. A week lagged wastewater SARS-CoV-2 did not show any significant association with COVID-19 mortality. Our results indicate that the association between time-lagged wastewater SARS-CoV-2 and COVID-19 hospital admission varied by patients characteristics, suggesting variations in SARS-CoV-2 shedding by patients characteristics. These findings warrant to incorporate patient-specific demographic characteristics and comorbidities in modelling infectious diseases surveillance using wastewater monitoring of the infectious agents.

The Province of Ontario (Canada) experienced a generational scale outbreak of measles in 2025. We applied wastewater surveillance concurrently with clinical-based surveillance to track measles incidence in southwestern Ontario adjacent to the United States. Measles virus (MeV) signal in wastewater was positively associated with clinical cases but did not provide early alert of changes in measles incidence when resolved by epidemiological week. Assessment of virus partitioning showed MeV RNA was broadly distributed in the liquid phase but is most concentrated in the solids. An assay was adapted for differentiation of vaccine and wildtype MeV and used to detect vaccine genotype measles following an inoculation campaign targeting underserved groups in the region. MeV shedding in wastewater was estimated through repeated sampling of sewer laterals serving a hospital treating confirmed measles infections. This measles outbreak serves as a case study highlighting the application of wastewater surveillance for measles while supporting method development in real-time.

Robust early warning of emerging viruses requires sampling populations that drive spread coupled with assays capable of detecting new viral variants or species. Untargeted viral metagenomic sequencing can, in principle, detect any virus, including completely novel ones. Composite airplane wastewater enables monitoring long-distance travelers from central collection points; however, the performance of untargeted viral metagenomic sequencing in this sample type remains unknown. In municipal wastewater, abundant sewer-associated microbes and ribosomal RNA depress viral relative abundance, limiting metagenomic sensitivity. We compared untargeted viral metagenomic sequencing of composite airplane wastewater with time-matched municipal wastewater from the Greater Boston area. Human viruses and other human-associated taxa had consistently higher relative abundance in airplane samples than municipal samples, while most sewer-associated taxa had lower relative abundance. An increased relative abundance of human viruses lowers the sequencing depth required to detect emerging pathogens, suggesting that metagenomic sequencing of composite airplane wastewater is a cost-effective method for pathogen-agnostic surveillance. ImportanceLong-distance air travelers spread viral pathogens globally, making them an ideal sentinel population for pandemic surveillance systems. Testing composite airplane wastewater offers a practical, noninvasive approach to monitoring the traveler population. However, current surveillance systems rely on tests targeting specific known pathogens, missing novel threats. Untargeted metagenomic sequencing can detect viruses known or novel, but remains expensive to implement at scale; in municipal wastewater, sewer-derived microbes tend to overwhelm human viruses in sequencing data. We investigated whether a hypothesized reduced sewer microbial load in airplane wastewater would lower the sequencing effort required for viral detection. Understanding the performance of metagenomic sequencing in this context informs the design of cost-effective early-warning systems for emerging pandemics.

Wastewater surveillance has been widely adopted since the COVID-19 pandemic, but non-sewered or onsite sanitation is a common form of sanitation in cities of low- and middle-income countries. Environmental surveillance in these settings requires expanding analyses beyond wastewater. We collected 81 soil samples adjacent to public waste bins inside the sewered and non-sewered areas of Maputo and a 150-meter-wide buffer zone between the two areas, as well as from subsistence farms near the wastewater treatment plant for comparison. We cultured Escherichia coli (E. coli) using the IDEXX Quanti-Tray/2000 system and determined the prevalence of 29 unique enteric pathogens via RT-qPCR on TaqMan array cards. E. coli concentrations were significantly higher (p<.001) in soils adjacent to public waste bins (mean = 5.05x105 per gram) compared to soils from farms (mean = 8.70x101 per gram). The mean number of unique pathogens was higher in soils from the non-sewered area (mean = 7.9, n=32) and the 150-meter buffer area (mean = 10.5, n=10) compared to the sewered area (mean = 4.6, n=20) and soils from farms (mean=3.8, n=19). Findings demonstrate that the presence of enteric pathogens in soils adjacent to public waste bins were associated with neighborhood sanitation infrastructure and may be a useful matrix for surveillance. In high-burden settings with poor sanitation, direct examination of soils and other environmental matrices are potentially scalable means of environmental pathogen surveillance to consider beyond conventional sampling matrices.

Wastewater monitoring enables non-invasive, population-scale tracking of community infections independent of healthcare-seeking behavior and clinical diagnosis. Metagenomic sequencing extends this capability by enabling broad, pathogen-agnostic detection, genomic characterization, and identification of novel or unexpected threats. Here, we present data from CASPER (the Coalition for Agnostic Sequencing of Pathogens from Environmental Reservoirs), a U.S.-based wastewater metagenomic sequencing network designed for deep, untargeted pathogen monitoring at national scale. This release includes 1,206 samples collected between December 2023 and December 2025 from 27 sites across nine states, covering 13 million people. Deep sequencing (~1 billion read pairs per sample) generated 1.2 trillion read pairs (347 terabases), enabling detection of even rare taxa, with CASPER representing 66% of all untargeted wastewater sequencing data currently available on the NCBI Sequence Read Archive. Virus abundance trends correlate with nationwide wastewater PCR and clinical data for SARS-CoV-2, influenza A, and respiratory syncytial virus, while the pathogen-agnostic approach captures emerging threats, including avian influenza H5N1 during initial dairy cattle outbreaks, West Nile virus, and measles, among hundreds of viral taxa. As the largest publicly available untargeted wastewater sequencing dataset to date, CASPER provides a shared and growing resource for pathogen surveillance and microbial ecology.

Candida auris is a multidrug-resistant fungal pathogen that presents substantial challenges for healthcare facilities due to its high mortality rates among vulnerable populations. Six C. auris clades have been identified based on their susceptibility to antifungal treatment and environmental stressors. Identifying the circulating C. auris clade(s) is critical for understanding transmission and selecting a disease control strategy. To inform targeted implementation of community wastewater monitoring for C. auris, samples were collected over 34 weeks from 8 nursing homes and 6 downstream wastewater treatment plants (WWTPs). Detection rates and concentrations of C. auris DNA were significantly higher in samples from nursing homes compared to those from WWTPs. Amplicon sequencing methods were developed and applied to characterize the circulating C. auris clade in a nursing home wastewater sample. This study demonstrates the utility of wastewater monitoring as a resource-efficient approach for detecting and subtyping C. auris in vulnerable communities.

We present findings from the first known pilot study of transatlantic airplane wastewater monitoring, conducted over six months at two connected international airports in the United States and the United Kingdom. This study demonstrates the feasibility of implementing bilateral wastewater-based pathogen surveillance at international travel hubs. We outline the operational and analytical methodologies employed, highlight key challenges encountered in transnational coordination, and provide recommendations for the design and implementation of future surveillance programs at points of entry.

Hepatitis E virus (HEV) is considered a predominantly foodborne pathogen in developed settings. During COVID-19 lockdown periods, however, HEV concentrations in wastewater at a treatment plant in Munich, Germany decreased, suggesting that pandemic-related behaviour changes inadvertently influenced transmission. In contrast, reported cases and wastewater data from a smaller catchment showed no comparable decline. To assess whether the observed reduction is compatible with a near-exclusively foodborne infection and to reconcile the contrasting signals across surveillance modalities, we developed a stochastic, individual-level model of HEV transmission, shedding, and ascertainment in Munich. Using Approximate Bayesian Computation, we calibrated this model to wastewater and case data from 2020-2023, first separately and then jointly. Posterior parameter estimates indicated a substantial decline in transmission during lockdowns to about 35-40% of the non-lockdown level, with the 95% credible interval entirely below 1 (no change). Joint inference suggested that possible modest lockdown-associated increases in diagnosis probabilities and higher measurement variability in the smaller catchment masked this effect in clinical and small-scale wastewater data, respectively. These findings demonstrate how wastewater-based surveillance, used alongside reported cases, can enable more robust parameter inference for models of under-reported pathogens like HEV, thereby supporting informed public health risk assessments.

Measles outbreaks have surged globally in recent years, but current surveillance systems have limited capacity to monitor measles virus (MeV) transmission and evolution at population scale. Although MeV can be detected in wastewater, the public health potential of wastewater genomic surveillance for MeV remains largely unexplored. Here, we deploy sensitive, low-cost MeV wastewater genomic surveillance combining virus concentration, whole-genome amplicon sequencing, and bioinformatic analysis alongside routine clinical genomic surveillance during the 2024-25 outbreak in South Africa. Integrated phylogenetic analyses of wastewater and clinical MeV genomes revealed previously undetected interprovincial spread and transmission links not captured by standard N450 sequencing. Our findings demonstrate that wastewater-integrated whole-genome surveillance expands the coverage and resolution of routine MeV monitoring and provides a scalable tool to advance measles control and elimination efforts.

Wastewater is increasingly being recognized as an important data stream that can contribute to infectious disease surveillance and forecasting. With this recognition, a growing number of statistical inference approaches are being developed to use wastewater data to provide quantitative insights into epidemiological dynamics. However, few existing approaches have allowed for systematic integration of data streams for inference, for example by combining case incidence data and/or serological data with wastewater data. Furthermore, only a subset of existing approaches have been able to handle missing data without imputation and to handle datasets with different sampling times or intervals. Here, we develop a statistically rigorous, yet lightweight, approach to infer and forecast time-varying effective reproduction numbers (Rt values) using longitudinal wastewater virus concentrations either alone or jointly with additional data streams including case incidence data and serological data. Our approach relies on a state-space modeling approach for inference and forecasting, within the context of a simple bootstrap particle filter. We first describe the structure of our underlying disease transmission process model as well as our observation models. Using a mock dataset, we then show that Rt can be accurately estimated by interfacing this model with case incidence data, wastewater data, or a combination of these two data streams using the bootstrap particle filter. Of note, we show that these data streams alone do not allow for reconstruction of underlying infection dynamics due to structural parameter unidentifiability. We then apply our particle filter to a previously analyzed SARS-CoV-2 dataset from Zurich that includes case data and wastewater data. Our analyses of these real-world datasets indicate that incorporation of process noise (in the form of environmental stochasticity) into the state space model greatly improves our ability to reconstruct the latent variables of the model. We further show that underlying infection dynamics can be made identifiable through the incorporation of serological data and that the bootstrap particle filter can be used to make forecasts of Rt, case incidence, and wastewater virus concentrations. We hope that the inference approach presented here will lead to greater reliance on wastewater data for disease surveillance and forecasting that will aid public health practitioners in responding to infectious disease threats.

The global increase in microbial infections is strongly linked to the widespread dissemination of antibiotic resistance genes in the environment, posing a serious challenge to public health. In the present study, surface water samples were collected from multiple locations along the Daya River near Bhubaneswar, including sites impacted by untreated municipal sewage discharge. The predominant bacterial isolate was identified as Acinetobacter baumannii-exhibited resistance to commonly used antibiotics such as ampicillin and ciprofloxacin. These findings highlight the urgent need for improved sewage treatment and waste management practices to prevent contamination of riverine systems and limit the spread of antibiotic-resistant bacteria.

Wastewater-based epidemiology (WBE) is an effective surveillance approach for monitoring viruses of public health relevance at the community level, complementing clinical surveillance systems. Molecular methods such as PCR/qPCR are widely used for targeted detection, while next-generation sequencing (NGS) with targeted enrichment panels has emerged as a complementary strategy for broader viral detection and genomic characterization. This study comparatively evaluated conventional PCR/qPCR and a targeted enrichment whole-genome sequencing Viral Surveillance Panel (VSP, Illumina) for virus detection in wastewater. Fifty-six wastewater samples collected between 2017 and 2023 from a wastewater treatment plant in Cordoba, Argentina, were concentrated by polyethylene glycol precipitation and pooled by season and year, reaching a total of 14 pools. Each pool was analyzed in parallel by PCR/qPCR for eight human viruses of public health importance and by the VSP, targeting 66 viral species, sequenced on a NovaSeq 6000 platform, and analyzed with the DRAGEN pipeline. Detection frequencies for each virus using PCR/qPCR and VSP were: RoV A 100%/14.3%; NoV 100%/14.3%; AiV 50%/42.9%; SARS-CoV-2 14.3%/0%; HAV 42.9%/0%; HEV 14.3%/0%; JCPyV 35.7%/85.7%; BKPyV 28.6%/71.4%, respectively. In addition, VSP detected the genomes of Astrovirus (71.4%), Salivirus (21.4%), Coxsackie A (14.3%), Rotavirus C (14.3%), and Merkel Cell virus (7.1%), and enable the recovery of 16 near complete genomes (coverage > 92.5%) of AiV, JCPyV, BKPyV, Salivirus and Astrovirus. PCR/qPCR and targeted enrichment NGS provide complementary information wastewater viral surveillance. Their combined application improves virus detection and genomic characterization, reinforcing the value of integrated approaches in environmental virology and public health monitoring.

Antimicrobial resistance (AMR) is a growing global public health threat projected to cause up to 10 million deaths annually by 2050 if no immediate action is taken. While misuse and overuse of antibiotics are the main drivers of increasing AMR, the eco-evolutionary dynamics of AMR in the environment - particularly across the urban-rural continuum - remain poorly understood. Using shotgun sequencing, we investigated urban, farm, and rural water sources in the Berlin-Brandenburg region to explore the distinctness or overlap of their antibiotic resistance gene (ARG) profiles and the potential impact of wastewater treatment plants (WWTP). ARGs were identified using multiple databases and five bioinformatic tools, combining sequence-based alignment and deep learning approaches. This multi-tool approach allowed for the detection of up to 18 AMR classes--more than any single tool alone. The multi-tool screening approach for ARGs, combined with the ABRicate algorithm, was superior to all single ARG tools and databases, detecting more AMR classes, allowing for biocide and metal resistance detection, while less sensitive for detection of aminocoumarin resistance genes. ARG diversity was higher in urban lake sediments, urban waters, and wastewater compared to rural lake sediments and water. Among all environments, urban lake water showed the highest overall ARG abundance, second only to wastewater, and this pattern held across all AMR classes, except for aminoglycoside resistance, which was most prevalent in rural lake sediments. The WWTP was unable to remove the circulating pool of ARGs, despite a decrease in unique ARGs in the outflow.