Wastewater surveillance for viral pathogens: A tool for public health

Hotspots and Trends in Research on Early Warning of Infectious Diseases: A Bibliometric Analysis Using CiteSpace

Background: Emerging and re-emerging infectious diseases (EIDs and Re-EIDs) cause significant economic crises and public health problems worldwide. Epidemics appear to be more frequent, complex, and harder to prevent. Early warning systems can significantly reduce outbreak response times, contributing to better patient outcomes. Improving early warning systems and methods might be one of the most effective responses. This study employs a bibliometric analysis to dissect the global research hotspots and evolutionary trends in the field of infectious disease early warning, with the aim of providing guidance for optimizing public health emergency management strategies. Methods: Publications related to the role of early warning systems in detecting and responding to infectious disease outbreaks from 1999 to 2024 were retrieved from the Web of Science Core Collection (WoSCC) database. CiteSpace software was used to analyze the datasets and generate knowledge visualization maps. Results: A total of 798 relevant publications are included. The number of annual publications has sharply increased since 2000. The USA produced the highest number of publications and established the most extensive cooperation relationships. The Chinese Center for Disease Control & Prevention was the most productive institution. Drake, John M was the most prolific author, while the World Health Organization and AHMED W were the most cited authors. The top two cited references mainly focused on wastewater surveillance of SARS-CoV-2. The most common keywords were "infectious disease", "outbreak", "transmission", "virus", and "climate change". The basic keyword "climate" ranked the first and long duration with the strongest citation burst. "SARS-CoV-2", "One Health", "early warning system", "artificial intelligence (AI)", and "wastewater-based epidemiology (WBE)" were emerging research foci. Conclusions: Over the past two decades, research on early warning of infectious diseases has focused on climate change, influenza, SARS, virus, machine learning, warning signals and systems, artificial intelligence, and so on. Current research hotspots include wastewater-based epidemiology, sewage, One Health, and artificial intelligence, as well as the early warning and monitoring of COVID-19. Research foci in this area have evolved from focusing on climate-disease interactions to pathogen monitoring systems, and ultimately to the "One Health" integrated framework. Our research findings underscore the imperative for public health policymakers to prioritize investments in real-time surveillance infrastructure, particularly wastewater-based epidemiology and AI-driven predictive models, and strengthen interdisciplinary collaboration frameworks under the One Health paradigm. Developing an integrated human-animal-environment monitoring system will serve as a critical development direction for early warning systems for epidemics.

Epidemiological and Molecular Surveillance of Aichi Virus A at Different Stages of Sewage Treatment: A One-Year Study in the Southeast of Brazil

Enteric viruses, such as the Aichi virus (AiV), pose a potential health risk due to their high excretion rates through fecal elimination, limited removal during treatment processes, and prolonged survival, highlighting the need to assess the potential for exposure and disease transmission through sanitation systems. This study investigated the prevalence of AiV at three key stages of sewage treatment in the city of São José do Rio Preto, São Paulo state, Brazil, as well as its viral concentrations, infectious potential, and molecular characterization. The data were also analyzed for potential correlations with reported diarrheal disease cases in the city and the physicochemical properties of sewage. The methodology employed included Nested PCR, qPCR, Sanger Sequencing, and phylogenetic analysis, as well as infectivity testing in cell cultures. The prevalence of AiV throughout the year in raw sewage samples was 90.4%, 78.8% in post-anaerobic biological treatment, and 71.1% in post-chemical treatment, totaling 125 positive samples out of 156, being characterized as AiV genotype A. The virus also demonstrated persistence and infectious potential at all three stages analyzed. The AiV-A mean concentration ranged from 2.05 log10 to 4.64 GC/mL, 2.31 to 4.72 log10 GC/mL, and 2.13 to 2.85 log10 GC/mL for the same treatment stages, respectively. A significant difference (p ≤ 0.05) suggests higher viral concentrations in summer at the three sewage process points analyzed, while lower viral concentrations were observed in post-chemical treatment samples (p ≤ 0.01). Additionally, no statistically significant relationship was observed between the virus occurrence in samples and cases of acute diarrheal diseases in the city. In conclusion, this study highlights that much remains to be understood about AiV while providing valuable insights into the relationship between AiV, environmental factors, and public health.

Wastewater Metavirome Diversity: Exploring Replicate Inconsistencies and Bioinformatic Tool Disparities

This study investigates viral composition in wastewater through metagenomic analysis, evaluating the performance of four bioinformatic tools-Genome Detective, CZ.ID, INSaFLU-TELEVIR and Trimmomatic + Kraken2-on samples collected from four sites in each of two wastewater treatment plants (WWTPs) in Lisbon, Portugal in April 2019. From each site, we collected and processed separately three replicates and one pool of nucleic acids extracted from the replicates. A total of 32 samples were processed using sequence-independent single-primer amplification (SISPA) and sequenced on an Illumina MiSeq platform. Across the 128 sample-tool combinations, viral read counts varied widely, from 3 to 288,464. There was a lack of consistency between replicates and their pools in terms of viral abundance and diversity, revealing the heterogeneity of the wastewater matrix and the variability in sequencing effort. There was also a difference between software tools highlighting the impact of tool selection on community profiling. A positive correlation between crAssphage and human pathogens was found, supporting crAssphage as a proxy for public health surveillance. A custom Python pipeline automated viral identification report processing, taxonomic assignments and diversity calculations, streamlining analysis and ensuring reproducibility. These findings emphasize the importance of sequencing depth, software tool selection and standardized pipelines in advancing wastewater-based epidemiology.

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

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

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.

Wastewater-Based Epidemiological Surveillance in France: The SUM'EAU Network

Wastewater surveillance is a powerful public health tool which gained global prominence during the COVID-19 pandemic. This article describes the development and implementation of the national wastewater surveillance network in France: SUM'EAU. Preliminary work included defining a sampling strategy, evaluating/optimising analytical methods, launching a call for tenders to select network laboratories and producing wastewater monitoring indicators. SUM'EAU was then deployed in three stages: (i) a pilot study, (ii) the transfer of analytical activities from the National Reference Laboratory to four selected network laboratories, and (iii) the extension of the system to additional sampling sites. Currently, SUM'EAU monitors SARS-CoV-2 across 54 wastewater treatment plants in mainland France. Once a week on business days, 24 h flow-proportional composite samples are collected at plant inlets and transported at 5 °C (±3 °C) to partner laboratories for analysis. The analytical process involves sample concentration, RNA extraction, and digital RT-PCR/q-RT-PCR to detect and quantify the presence of the SARS-CoV-2 genome in wastewater. Subsequently, data are transferred to Santé publique France, the French National Public Health Agency, for analysis and interpretation. While SUM'EAU has been instrumental in monitoring the COVID-19 pandemic and holds significant potential for broader application, securing sustainable funding for its operation remains a major challenge.

Optimized Aluminum Hydroxide Adsorption-Precipitation for Improved Viral Detection in Wastewater

Wastewater-based epidemiology (WBE) is a valuable tool for monitoring pathogen spread in communities; however, current protocols mainly target non-enveloped viruses. This study addresses the need for standardized methods to detect both enveloped and non-enveloped viruses by testing four aluminum hydroxide adsorption-precipitation techniques. Wastewater samples were spiked with an enveloped virus surrogate (Φ6 bacteriophage) and a non-enveloped virus surrogate (MS2 coliphage), and viral recovery was assessed using reverse-transcription quantitative PCR (RT-qPCR). The highest recovery for the enveloped virus was achieved with AlCl3 at pH 3.5, a 15 min flocculation time, and a 3% elution solution concentration. For the non-enveloped virus, optimal recovery was found with AlCl3 at pH 6.0, no flocculation time, and a 10% elution solution. The best method for recovering both virus types used AlCl3 at pH 6.0, 15 min flocculation, and a 3% elution solution concentration. This study shows that while optimal conditions vary between virus types, a standardized AlCl3 flocculation protocol can efficiently recover both, providing a cost-effective approach for outbreak monitoring in Grenada.