Search Results (203)

Environmental DNA (eDNA) analysis has emerged as a transformative tool in environmental monitoring, enabling non-invasive detection of species and microbial communities across diverse ecosystems. This study systematically reviews the role of bioinformation technology in eDNA analysis, focusing on methodologies and applications across air, soil, groundwater, sediment, and aquatic environments. Advances in molecular biology, high-throughput sequencing, bioinformatics tools, and field-deployable detection systems have significantly improved eDNA detection sensitivity, allowing for early identification of invasive species, monitoring ecosystem health, and tracking pollutant degradation processes. Airborne eDNA monitoring has demonstrated potential for assessing microbial shifts due to air pollution and tracking pathogen transmission. In terrestrial environments, eDNA facilitates soil and groundwater pollution assessments and enhances understanding of biodegradation processes. In aquatic ecosystems, eDNA serves as a powerful tool for biodiversity assessment, invasive species monitoring, and wastewater-based epidemiology. Despite its growing applicability, challenges remain, including DNA degradation, contamination risks, and standardization of sampling protocols. Future research should focus on integrating eDNA data with remote sensing, machine learning, and ecological modeling to enhance predictive environmental monitoring frameworks. As technological advancements continue, eDNA-based approaches are poised to revolutionize environmental assessment, conservation strategies, and public health surveillance. Full article

Antimicrobial resistance (AMR) has emerged as a planetary health emergency, driven not only by the clinical misuse of antibiotics but also by diverse environmental dissemination pathways. This review critically examines the role of environmental compartments—water, soil, and air—as dynamic reservoirs and transmission routes for antibiotic-resistant bacteria (ARB) and resistance genes (ARGs). Recent metagenomic, epidemiological, and mechanistic evidence demonstrates that anthropogenic pressures—including pharmaceutical effluents, agricultural runoff, untreated sewage, and airborne emissions—amplify resistance evolution and interspecies gene transfer via horizontal gene transfer mechanisms, biofilms, and mobile genetic elements. Importantly, it is not only highly polluted rivers such as the Ganges that contribute to the spread of AMR; even low concentrations of antibiotics and their metabolites, formed during or after treatment, can significantly promote the selection and dissemination of resistance. Environmental hotspots such as European agricultural soils and airborne particulate zones near wastewater treatment plants further illustrate the complexity and global scope of pollution-driven AMR. The synergistic roles of co-selective agents, including heavy metals, disinfectants, and microplastics, are highlighted for their impact in exacerbating resistance gene propagation across ecological and geographical boundaries. The efficacy and limitations of current mitigation strategies, including advanced wastewater treatments, thermophilic composting, biosensor-based surveillance, and emerging regulatory frameworks, are evaluated. By integrating a One Health perspective, this review underscores the imperative of including environmental considerations in global AMR containment policies and proposes a multidisciplinary roadmap to mitigate resistance spread across interconnected human, animal, and environmental domains. Full article

Wastewater-based epidemiology (WBE) has historically proven to be a powerful surveillance tool, particularly during the SARS-CoV-2 pandemic. Effective WBE depends on the sensitive detection of pathogens in wastewater. However, determining the process limit of detection (PLOD) of WBE through a comprehensive evaluation that accounts for pathogen concentration, nucleic acid extraction, and molecular analysis has rarely been documented. We prepared dilution series with known concentrations of S. Typhi, V. cholerae, rotavirus, and SARS-CoV-2 in surface water and wastewater. Pathogen concentration was performed using Nanotrap particles with the KingFisher™ Apex robotic platform, followed by nucleic acid extraction. Quantitative real-time PCR (qPCR) and digital PCR (dPCR) were used to detect the extracted nucleic acids of the pathogens. The PLODs and recovery efficiencies for each of the four pathogens in surface water and wastewater were determined. Overall, the observed PLODs for S. Typhi, V. cholerae, and rotavirus in surface water and wastewater were approximately 3 log₁₀ loads (2.1–2.8 × 10³/10 mL) using either qPCR or dPCR as the detection method. For SARS-CoV-2, the PLOD in surface water was 2.9 × 10⁴/10 mL with both RT-qPCR and dPCR, one log₁₀ higher than the PLODs of the other three pathogens. In wastewater, the PLOD for SARS-CoV-2 was 2.9 × 10⁴/10 mL using RT-qPCR and 2.9 × 10³/10 mL using dPCR. The mean recovery rates of S. Typhi, V. cholerae, rotavirus, and SARS-CoV-2 for dPCR in both surface water and wastewater were below 10.4%, except for S. Typhi and V. cholerae in wastewater, which showed significantly higher recoveries, from 26.5% at 4.6 × 10⁵/10 mL for S. Typhi to 58.8% at 4.8 × 10⁵/10 mL for V. cholerae. Our study demonstrated that combining qPCR or dPCR analysis with automated Nanotrap particle concentration and nucleic acid extraction using the KingFisher™ platform enables the sensitive detection of S. Typhi, V. cholerae, rotavirus, and SARS-CoV-2 in surface water and wastewater. Full article

Streptococcus pyogenes is a bacterial pathogen known to be the causative agent in many different illnesses, with Group A Streptococcus (GAS) pharyngitis (strep throat), being one of the more prevalent. The spread and severity of GAS pharyngitis can grow exponentially if individuals are not taking the proper precautions. Wastewater surveillance has been used to test for numerous different pathogens that humans spread throughout a community and in this study, we utilized wastewater surveillance to monitor GAS pharyngitis in a small city. Over a year, 57 wastewater influent samples were tested for S. pyogenes and three commonly tested respiratory viruses (Respiratory Syncytial Virus (RSV), SARS-CoV-2, Influenza A). Three microbial indicators and population normalizers (CrAssphage, Pepper mild mottle virus (PMMoV), and Mycobacterium) were tested as well to compare and contrast each indicator’s value and range over time. Wastewater data was then compared to publicly available search term data as clinical data was not readily available. There was a high correlation between the collected molecular data and the publicly available search term data for Streptococcus pyogenes. Additionally, this study provided more information about the seasonal trend of S. pyogenes throughout the year through molecular data and allowed for the ability to track peak infection months in this small city. Overall, these results highlight the substantial benefits of using wastewater surveillance for the monitoring of GAS pharyngitis. This study also provides helpful insights into future studies about the prevalence of respiratory bacteria and their seasonal trends in wastewater, allowing for public health systems to provide mitigation strategies. Full article

The rapid expansion of infectious diseases in urban environments presents a significant public health challenge, as traditional surveillance methods rely on delayed case reporting, limiting proactive response capabilities. With the increasing availability of real-time health data, artificial intelligence (AI) has emerged as a powerful tool for disease monitoring, anomaly detection, and outbreak prediction. This study proposes SmartHealth-Track, an AI-powered real-time infectious disease monitoring framework that integrates machine learning models with IoT-enabled surveillance, smart pharmacy analytics, wearable health tracking, and wastewater surveillance to enhance early outbreak detection and predictive forecasting. The system leverages time series forecasting with long short-term memory (LSTM) networks, logistic regression for outbreak probability estimation, anomaly detection with isolation forests, and natural language processing (NLP) for extracting epidemiological insights from public health reports and social media trends. Experimental validation using real-world datasets demonstrated that SmartHealth-Track achieves high accuracy, with an outbreak detection accuracy of 92.4%, wearable-based fever detection accuracy of 93.5%, AI-driven contact tracing precision of 91.2%, and AI-enhanced wastewater pathogen classification accuracy of 94.1%. The findings confirm that AI-driven real-time surveillance significantly improves outbreak detection and forecasting, enabling timely public health interventions. Future research should focus on federated learning for secure data collaboration and reinforcement learning for adaptive decision making. Full article

In Mexico, hospital wastewater (HWW) is a source of chemical and microbiological contamination, and it is released into the municipal sewage system without prior treatment. This water may contain pathogenic bacteria and antimicrobial resistance genes, which represent a risk to Public Health and the environment. So far, there are no studies that analyse this problem comprehensively, relating bacterial population structures, chemical contaminants, and seasonality. The aim of this work was to seasonally characterise the bacterial communities of HWW, including clinically relevant bacteria and resistance genes in Hospital Juárez de México (HJM), and to evaluate the impact of physicochemical factors on their composition. A one-year observational, cross-sectional study was conducted at five HWW discharge points of HJM. Fourteen physicochemical parameters were determined by using standard methodologies, and statistical differences between discharges and seasons were evaluated. Bacterial communities were analysed by targeted amplicon sequencing of the V3-V4 region of the 16S rRNA gene. In addition, the presence of eight antimicrobial resistance genes of local epidemiological importance was assessed. Data were analysed using alpha and beta diversity indices, principal component analysis, and multivariate statistical tests. HWW showed high taxonomic diversity, with Proteobacteria, Firmicutes, and Bacteroidetes standing out. Clinically relevant bacteria were identified in 73.3% of the analyses, with Enterobacter and Escherichia-Shigella predominating. Total and dissolved solids, temperature, nitrate, and pH significantly influenced the bacterial composition of HWW. Seven out of the eight genes evaluated were identified, with bla_KPC, bla_OXA-40, and mcr-1 being the most frequent, showing significant seasonal differences. This study underlines the microbiological and chemical complexity of HWW, highlighting the impact of clinically relevant bacteria and antimicrobial resistance genes on Public Health. The findings emphasise the need to implement hospital waste management programmes and ideally specific treatment plants to minimise the associated risks and protect the environment and human health. Full article

Wastewater-based epidemiology (WBE) offers a promising tool for sexually transmitted infection (STI) surveillance, especially in settings where underdiagnosis or social stigma complicates conventional reporting. To assess its utility, we conducted a year-long study examining six STIs, Chlamydia trachomatis, Treponema pallidum, Neisseria gonorrhoeae, human immunodeficiency virus (HIV), hepatitis C virus (HCV), and herpes simplex virus (HSV), in weekly composite samples from the primary influent of a small-sized Midwestern wastewater treatment plant. Pathogen detection and quantification were performed via digital PCR. Among the tested targets, Gonorrhea, HIV, HCV, and HSV were detected at the highest frequencies, often in 40–50% of the samples, while Chlamydia and Syphilis appeared less frequently. Despite the variability in detection patterns, this study demonstrates that even infrequent signals can reveal community-level shedding of poorly reported or asymptomatic infections. Although month-to-month wastewater data were not strongly correlated with corresponding clinical records, which could potentially reflect delayed healthcare seeking and pathogen-specific shedding dynamics, the overall findings underscore WBE’s ability to complement existing surveillance by capturing infections outside traditional healthcare channels. These results not only advance our understanding of STI prevalence and population shedding but also highlight the practical benefits of WBE as an early warning and targeted intervention tool. Full article

Wastewater-based epidemiology (WBE) offers valuable insight into viral circulation at the community level. In this study, we combined digital PCR (dPCR) with molecular typing to investigate the prevalence and diversity of human adenoviruses (HAdVs) in untreated wastewater samples collected throughout Italy. HAdV genomes were detected in over 93% of the 168 samples analyzed, with concentrations up to 4.5 × 10⁶ genome copies per liter. For genotypic characterization, we used nested PCR followed by Sanger and Oxford Nanopore Technologies (ONTs) long-read sequencing. While Sanger sequencing identified three dominant genotypes (HAdV-A12, HAdV-B3, and HAdV-F41), ONT sequencing provided enhanced resolution, confirming all previously identified types and revealing seven additional genotypes: HAdV-B21, HAdV-C5, HAdV-D45, HAdV-D46, HAdV-D49, HAdV-D83, and HAdV-F40. This comprehensive approach highlights the added value of ONT long-read sequencing in uncovering the genetic complexity of adenoviruses in wastewater, particularly in detecting rare or low abundance types that conventional methods may miss. Our findings highlight the value of integrating quantitative and high-resolution molecular tools in WBE to improve surveillance and better understand the epidemiology of viral pathogens circulating in the human population. Full article

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. Full article

Antimicrobial resistance (AMR) is an emerging global threat that is expanding in many areas of the world. Wastewater-based epidemiology (WBE) is uniquely suited for use in areas of the world where clinical surveillance is limited or logistically slow to identify emerging threats, such as in Sub-Saharan Africa (SSA). Wastewater was analyzed from three urban areas of Kampala, including a local HIV research clinic and two informal settlements. Wastewater extraction was performed using a low-cost, magnetic bead-based protocol that minimizes consumable plastic consumption followed by sequencing on the Oxford Nanopore Technology MinION platform. The majority of the analysis was performed using cloud-based services to identify AMR biomarkers and bacterial pathogens. Assemblies containing AMR pathogens were isolated from all locations. As one example, clinically relevant AMR biomarkers for multiple drug classes were found within Acinetobacter baumannii genomic fragments. This work presents a metagenomic WBE workflow that is compatible with areas of the world without robust water treatment infrastructure. This study was able to identify various bacterial pathogens and AMR biomarkers without shipping water samples internationally or relying on complex concentration methods. Due to the time-dependent nature of wastewater surveillance data, this work involved cross-training researchers in Uganda to collect and analyze wastewater for future efforts in public health development. Full article

Wastewater-based molecular epidemiology enables the surveillance of both symptomatic and asymptomatic individuals in a non-invasive, cost-effective, rapid, and early-detection manner. The use of wastewater analysis to monitor the prevalence of viral pathogens in a given population has increased significantly since the COVID-19 pandemic. These studies typically involve three main steps: viral concentration, nucleic acid extraction, and DNA/RNA quantification. However, the absence of a standardized methodology remains a major limitation, hindering result comparability across studies. Among the available viral concentration techniques, aluminum-based adsorption–precipitation is one of the most commonly used due to its simplicity, efficiency, and low cost. This study evaluates the robustness and variability of the viral concentration and nucleic acid extraction steps by implementing different process controls in wastewater samples across 122 independent experiments. Additionally, correlations between viral recovery efficiencies and relevant physicochemical parameters were also analyzed (n = 600). The results indicate that, despite the overall robustness of the method, the concentration step exhibits the highest variability (CV = 53.82%), which accounted for 53.73% of the overall variability. In addition, our results show that, on average, 0.65 logarithmic units were lost during the viral concentration step. Furthermore, viral recovery rates were influenced by seasonality and sample characteristics, while no significant correlation was observed with pH or conductivity. These findings highlight the importance of process controls, confirming the robustness of the methodology, and identifying key parameters that should be considered in future studies for improved data interpretation. Full article

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

Wastewater-based epidemiology (WBE) has been widely employed to track the spread of human pathogens; however, correlating wastewater data with clinical surveillance remains challenging due to population variability and environmental factors affecting wastewater composition. This study evaluated different SARS-CoV-2 normalization methods, comparing static population estimates with dynamic normalization based on common physicochemical parameters: chemical oxygen demand (COD), biochemical oxygen demand (BOD₅), and ammonia (NH₄-N). Wastewater samples were collected from four urban wastewater treatment plants (WWTPs) in northwestern Tuscany (Italy) from February 2021 to March 2023. The correlations between normalized viral loads and clinical COVID-19 cases were highest for static normalization (ρ = 0.405), followed closely by dynamic normalization using COD and BOD₅ (ρ = 0.378 each). Normalization based on NH₄-N was less effective. These findings suggest that chemical parameters, particularly COD and BOD₅, offer a valid alternative for viral normalization when population estimates or flow rate measurements are unavailable. These parameters provide a cost-effective and practical approach for improving WBE reliability, particularly in resource-limited settings. Our results reinforce the importance of normalization in WBE to enhance its representativeness and applicability for public health surveillance. Full article

SARS-CoV-2 high transmission and genomic mutations result in the emergence of new variants that impact COVID-19 vaccine efficacy and virus transmission by evading the host immune system. Wastewater-based epidemiology is an effective approach to monitor SARS-CoV-2 variants circulation in the population but is a challenge due to the presence of reaction inhibitors and the low concentrations of SARS-CoV-2 in this environment. Here, we aim to improve SARS-CoV-2 variant detection in wastewater by employing nested PCR followed by next-generation sequencing (NGS) of small amplicons of the S gene. Eight SARS-CoV-2 wastewater samples from Alegria Wastewater Treatment Plant, in Rio de Janeiro, Brazil, were collected monthly from February to September 2021. Samples were submitted to virus concentration, RNA extraction and nested PCR followed by NGS. The small amplicons were used to prepare libraries for sequencing without the need to perform any fragmentation step. We identified and calculated the frequencies of 29 mutations matching the Alpha, Beta, Gamma, Delta, Omicron, and P.2 variants. Omicron matching-mutations were detected before the lineage was classified as a variant of concern. SARS-CoV-2 wastewater sequences clustered with SARS-CoV-2 variants detected in clinical samples that circulated in 2021 in Rio de Janeiro. We show that sequencing of selected small amplicons of SARS-CoV-2 S gene allows the identification of SARS-CoV-2 variants matching mutations and their frequencies’ calculation. This approach may be expanded using customizing primers for additional genomic regions, in order to differentiate current variants. Approaches that allow us to learn how variants emerge and how they relate to clinical outcomes are crucial for our understanding of the dynamics of virus variants circulation, providing valuable data for public health management. Full article

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. Full article