Your new experience awaits. Try the new design now and help us make it even better

MINI REVIEW article

Front. Public Health, 01 October 2025

Sec. Infectious Diseases: Epidemiology and Prevention

Volume 13 - 2025 | https://doi.org/10.3389/fpubh.2025.1652535

This article is part of the Research TopicInnovative Approaches for the Early Detection and Rapid Response to Biothreat and Emerging Infectious AgentsView all 5 articles

Molecular epidemiology of aquatic environments: challenges from sampling to implementation of surveillance programs

  • Instituto de Investigaciones en Salud (INISA), Universidad de Costa Rica, San José, Costa Rica

Pathogens are introduced into wastewater through human and animal fecal discharge, ultimately contaminating aquatic environments such as rivers and beaches. Molecular tools are commonly used to track outbreak-related pathogens in wastewater due to numerous advantages such as enhanced sensitivity, speed, and specificity. However, many low- and middle-income countries (LMICs) face challenges in developing adequate sanitation infrastructure and accessing or implementing high-cost technologies, which hampers the integration of environmental surveillance into national and regional public health programs. This mini-review summarizes key challenges in applying molecular techniques for water-based epidemiological monitoring of waterborne pathogens in resource-limited settings. We examine obstacles related to sampling aquatic environments, including collecting samples from rivers and concentrating analytes from complex matrices such as wastewater and polluted river or beach waters, emphasizing the importance of preserving environmental representativeness. We provide a brief overview of the most widely used PCR-based technologies for detecting waterborne pathogens and antimicrobial resistance genes (ARGs), discussing their advantages and limitations. We also examine advanced high-throughput technologies, often inaccessible in LMICs, and emerging portable tools that may enhance detection where laboratory infrastructure is limited. Finally, through applied examples, we show how environmental data can make pathogen surveillance more accessible while bridging laboratory research with public health practice.

Graphical abstract
www.frontiersin.org

Graphical Abstract.

1 Introduction

Water consumption is essential for maintaining good health; however, in 2020 nearly 2 billion people lacked access to safely managed drinking water services, including hundreds of millions relying on unimproved sources or even surface water (1). This challenge is further compounded by the growing release of anthropogenic compounds, such as surfactants, hormones, antibiotics, and other pollutants, into aquatic environments (2, 3). Thus, contaminated ecosystems can serve as reservoirs and dissemination hubs for human pathogens and urban pollutants that spread through water bodies (4).

Several pathogens including bacteria, fungi, viruses and protozoan can be found in aquatic environments, leading to waterborne outbreaks of numerous diseases (5). These microorganisms are often released into wastewater through direct human and animal fecal discharge, which eventually flows into aquatic environments like rivers and beaches. Wastewater treatment plants (WWTPs) were designed to reduce suspended solids, organic matter and other contaminants harmful to public and ecosystem health. However, most WWTPs fail to significantly reduce microbiological loads, as disinfection steps are often omitted unless the wastewater is designated for regeneration (6).

Consequently, WWTPs have emerged as reservoirs that mediate the propagation of microorganisms, particularly antibiotic-resistant bacteria. The high-pressure environment created by the presence of contaminants such as antimicrobials, fosters the selection and retention of adaptive traits, such as antibiotic resistance genes (ARGs) (7, 8). Moreover, wastewater-based surveillance has been utilized to track many outbreak-related pathogens (including pathogenic viral strains) under different technologies, particularly molecular biology tools (9). Nevertheless, the integration of such strategies into governmental surveillance programs remains limited, particularly in LMICs where resources are scarce.

This review addresses key challenges in applying molecular techniques for waterborne pathogen surveillance in resource-limited regions. We also emphasize the importance of integrating molecular epidemiology strategies, framed within the One Health approach, into community and regional monitoring programs to mitigate pathogen spread and protect human health.

2 Sampling methods in aquatic environments

Molecular surveillance of waterborne pathogens requires robust sampling strategies tailored to the aquatic environment. Therefore, environmental representativeness and the integrity of genetic material are essential for obtaining reliable results. Factors such as matrix type, spatial and temporal variability, and the presence of inhibitors can influence sample quality and the accuracy of molecular analyses (1013). Consequently, key steps include appropriate sample collection and preservation, effective concentration of target microorganisms, and addressing challenges that may hinder the detection of pathogens in water (14, 15).

2.1 Preparation of samples

Obtaining a representative water sample is a critical first step in molecular epidemiology. Sampling strategies must account for the temporal and spatial variability of aquatic systems (1618). Grab samples, which are discrete samples collected at a single point in time, are simple and widely used. For instance, the World Health Organization recommends collecting at least 500 mL of grab samples from wastewater for poliovirus surveillance (19). However, short-duration grab samples may miss intermittent pathogen shedding events (2022). To improve environmental representativeness, composite sampling is often employed by combining subsamples over 24 h or across multiple sites to capture fluctuations in flow and contamination levels (23, 24). In wastewater surveillance, 24-h composite samples collected using automated samplers provide more stable estimates of pathogen load than random grab samples (25). Nonetheless, all samples must be collected in sterile containers and kept at low temperatures until processed to prevent degradation of genetic material (2628). Processing within 24–48 h or the addition of stabilizing agents, such as acidic pH buffers or RNA preservatives, is also recommended to prevent the degradation of pathogen nucleic acids, particularly in settings where immediate processing is not feasible (13, 29).

2.2 Filtration and concentration

Detecting waterborne pathogens often requires the processing of large water volumes, as these microorganisms are typically present in low concentrations (30). Therefore, several techniques have been developed to concentrate and filter microorganisms into smaller volumes (19). For instance, the most used methods for viruses include electropositive/electronegative filtration and ultrafiltration (31, 32). Among ultrafiltration approaches, hollow-fiber devices are capable of simultaneously recovering viruses, bacteria, and protozoa, with reported recovery efficiencies ranging from 70 to 90% for bacteria and protozoan oocysts (30, 33). However, one of their main disadvantages compared to other methods is their high cost (34).

Alternatively, precipitation-based methods have also been widely employed, such as the two-phase polyethylene glycol (PEG) flocculation protocol, recommended by the World Health Organization for the concentration of enteric viruses in wastewater (19, 35). In this approach, viruses are precipitated using PEG and salt, then resuspended in a small volume for downstream analysis (36). Similarly, aluminum hydroxide adsorption–precipitation and glycine beef extract elution are also used in specific protocols to concentrate viruses from environmental waters (37, 38). For bacterial pathogens, membrane filtration (e.g., 0.45 μm pore size filters) is routinely applied: microbes are retained on the membrane, which can then be used directly for DNA/RNA extraction or culture-based methods (39, 40).

2.3 Challenges in monitoring aquatic environments

One of the primary challenges in environmental surveillance is the spatial and temporal variability of pathogen presence, which can lead to false negatives if the sampling is not representative (41, 42). Furthermore, rainfall variability poses another challenge, as it can have direct and indirect effects on DNA detection, primarily by influencing sample dilution. Heavy rains can increase the water volume, followed by dilution of the DNA present in the environment sampled (43, 44). Additionally, there is no universal method capable of recovering all types of pathogens from water samples (12), primarily because viruses, bacteria, and protozoa differ in size and physicochemical properties, so protocols effective for one group may be inefficient for others. Moreover, filtration or elution processes often result in partial loss of pathogens, further compromising detection (45).

Environmental water samples also contain natural inhibitors, such as humic substances, fulvic acids, and phenolic compounds, that can interfere with PCR reactions, reducing the sensitivity of molecular detection methods (12). A significant limitation of molecular techniques like qPCR is their inability to differentiate between viable pathogens and residual nucleic acids from non-viable organisms, which complicates accurate risk assessment (46).

In LMICs, limited infrastructure, insufficient technical training, and restricted laboratory access further hinder the systematic implementation of these methods. Consequently, low-cost approaches with streamlined protocols should be prioritized to support continuous surveillance and produce actionable data for public health decision-making (47, 48).

3 PCR and LAMP methods for pathogens and target genes detection

Since the 1990s, polymerase chain reaction (PCR) has been widely adopted to amplify and detect viral and bacterial DNA in water samples, offering key advantages such as enhanced detection limits, reduced processing time and cost, and the ability to identify a broad range of microorganisms (10). Its high sensitivity, along with its specificity and reliability (12, 13), has established PCR as a cornerstone technique in molecular epidemiology studies of waterborne pathogens. Moreover, thermal cyclers, essential instruments for PCR, are now standard equipment in most molecular biology laboratories, including those in many LMICs.

End-point PCR has long been regarded as the gold standard for detecting various pathogens, including viruses and bacteria. Viral detection by conventional methods is often complex, typically requiring the concentration of viral particles and propagation in permissive host cells (10). In contrast, bacterial pathogens are generally easier to culture; however, traditional culture-based methods can be limited by difficulties in species-level identification and the presence of viable but non-culturable strains in environmental samples, increasing both the complexity and cost of detection (10). These limitations have made PCR-based methods highly valuable for accurate pathogen identification.

Although end-point PCR remains widely used, quantitative PCR (qPCR) has gained increasing traction due to its superior sensitivity, faster turnaround time, and its ability to simultaneously amplify, detect, and quantify specific nucleic acids. This enables more reproducible and timely assessments that support prompt public health interventions (14). qPCR works by detecting fluorescence emitted during DNA amplification, which correlates with the quantity of target DNA present in the sample. Fluorescence can be generated through intercalating dyes, such as SYBR Green, or through labeled probes containing a fluorescent reporter molecule that binds specifically to the target DNA (15).

Using qPCR can be helpful in wastewater-based surveillance, which plays an important role in early detection of diseases. Since the COVID-19 pandemic, this area of research has gained popularity due to its potential to detect prospective cases or outbreaks. A 2020 study conducted by a research group in Córdoba, Argentina, employed qPCR to detect SARS-CoV-2 genetic material in wastewater samples from multiple sites throughout the city. The findings of the study suggest that this monitoring approach is effective as an early warning system for future outbreaks in regions with a stable resident population. Conversely, in areas with low population density or significant population flux, such as those influenced by tourism, its utility may be limited to confirming the presence of the disease in the area (49). It is important to note that for wastewater-based surveillance to be effective, a thorough understanding of the sewer network’s infrastructure and the specific populations contributing to the sampled wastewater is essential. This requirement may pose a significant challenge in LMICs, where such information may be incomplete, inaccurate, or difficult to access (50).

Another PCR-based technique with high potential for use in molecular epidemiology is digital PCR (dPCR) (51, 52). Like qPCR, it relies on the detection of fluorescence resulting from DNA amplification. However, dPCR differs by partitioning the sample into thousands of individual reactions, each ideally containing a single DNA molecule. This allows for the absolute quantification of the initial DNA fragments with greater accuracy and sensitivity. In addition, dPCR has been reported to offer greater tolerance to inhibitors present in complex samples, which is particularly relevant for the implementation of DNA quantification methods in water samples (52). However, dPCR requires expensive reagents that are often not readily available in LMICs. These costs are further increased by importation taxes and shipping fees. Therefore, implementing dPCR in epidemiological programs using environmental samples in LMICs will require optimization of both reagent and equipment costs.

Finally, loop-mediated isothermal amplification (LAMP) is a variation of the PCR that does not require a thermal cycler instrument, as the entire process occurs at a constant temperature. It represents a promising alternative that is faster, simpler, and more accessible than conventional PCR methods. LAMP has been proposed as a low-cost option for the rapid identification of multiple pathogens (53); although several limitations remain.

LAMP requires the design of 4 to 6 primers targeting specific regions within a short DNA segment, which makes primer design complex. Additionally, due to the high efficiency of LAMP, the risk of false positives from contamination is considerable with improper handling. Furthermore, the turbidity- and colorimetric-based detection of LAMP-positive reactions is subjective, which may increase the likelihood of erroneous results (54, 55). Thus, although the method is promising, it will require further optimization and validation for its application in molecular epidemiology of environmental samples.

PCR-based methods have a limited ability to distinguish between pathogen variants/genotypes and to provide evolutionary insights, both of which are essential for tracking epidemiological patterns and understanding genetic flow. Although some qPCR probes have been developed to detect specific variants of pathogens such as SARS-CoV-2 (56, 57) and norovirus (58), the short length of these probes constrains their resolution. As a result, findings from such assays often require further confirmation using genetic/genomic data.

4 Genomic and metagenomic methods

Next,-generation sequencing (NGS) methods have been proposed as a promising solution for pathogen and antimicrobial resistance (AMR) surveillance (59, 60), particularly because genomic data can reveal critical evolutionary and epidemiological insights into outbreak dynamics, information beyond the reach of conventional PCR-based methods. Although the COVID-19 pandemic highlighted the urgency of implementing such methodologies, the capacity to apply NGS for genomic surveillance of environmental samples remains limited in LMICs due to budgetary constraints (61). Additionally, most programs prioritize the implementation of genomic surveillance in clinical settings, but fewer efforts allocate resources to the investigation of environmental samples (62).

Furthermore, the implementation of metagenomic approaches for genomic surveillance has been strongly encouraged (63, 64). Such techniques can improve the cost-efficiency per sample, as sufficient DNA quantity and sequencing depth allow the simultaneous detection of a wide range of pathogens and their effectors (virulence and ARGs). Similarly, microarrays offer a promising solution for multi-target detection by enabling the identification of hundreds of targets with high specificity through well-designed probes. For example, a DNA microarray was recently developed in Mexico to identify 252 etiological agents (with 38,000 probes) from environmental samples (65).

Following the SARS-CoV-2 pandemic, genomic surveillance has been increasingly implemented in LMIC to investigate epidemiological patterns of pathogens in water samples. For example, the co-circulation and abundance SARS-CoV-2 variants in wastewaters were monitored in Uruguay; serving as a complementary tool for tracking community-level transmission and informing early public health decisions (66). Building on this approach, genomic technologies have been adapted to study other pathogens beyond SARS-CoV-2. Also in Uruguay, a recent study applied wastewater-based genomic surveillance using targeted enrichment sequencing to monitor 42 respiratory viruses. They detected several pathogens that had not been previously reported in circulation (67).

While these technologies offer great potential, their high costs and the need for specialized bioinformatics expertise remain significant barriers, potentially limiting their accessibility in LMICs. These resource limitations underscore the need to establish region-wide networks that efficiently standardize multi-pathogen sequencing centers, facilitating the integration of genomic surveillance into regional public health policies (60).

5 Portable diagnostics and their impact on rapid response in LMICs

Portable diagnostic devices have been proposed as innovative tools for the rapid detection of infectious pathogens. These include point-of-care and molecular tests that enable the timely identification of pathogens and prompt treatment. Most of these portable devices have been validated using clinical samples (such as blood, urine, saliva, and other fluids), but some have even been tested on environmental water samples, as reviewed by Kumar et al. (68) and Oon et al. (62). These systems primarily rely on the detection of nucleic acids, proteins, or specific cell features of pathogens using biosensors and microfluidic systems.

The concept behind these devices is promising, especially for pathogen detection in areas where high-tech laboratories and trained personnel are unavailable, and where sample transportation can take several days. Although many of these devices have improved their sensitivity and specificity (62), they still face challenges when dealing with complex matrices such as environmental water samples, which often contain inhibitors. Moreover, compared to the previously mentioned gold standard methods, these devices have yet to be widely implemented or validated in surveillance programs. In addition, the cost per unit remains high, presenting a financial barrier in LMICs settings. While these tools offer promising solutions for rapid diagnostics, they simultaneously underscore the persistent economic constraints faced by resource-limited settings. Accordingly, the development and prioritization of low-cost, portable devices with validated protocols are essential to support sustained surveillance efforts and generate actionable data for informed public health decision-making (69).

6 Environmental and public health implications of molecular epidemiology of aquatic environments

Since its initial implementation for polio surveillance, environmental monitoring has been applied across multiple contexts to support public health efforts. Wastewater-based surveillance programs have demonstrated their effectiveness as early warning systems, helping to mitigate pathogen transmission and enabling the estimation of infection trends within populations (70).

Moreover, environmental surveillance of AMR offers a valuable opportunity to strengthen the One Health approach (71). This type of surveillance enables the identification of multidrug-resistant microorganisms, through the detection of resistance genes/markers, and also the potential environmental reservoirs of antimicrobial resistance genes (ARGs). Such information is critical to guide evidence-based decision-making regarding the prudent use of antimicrobials in human and veterinary medicine, as well as in industrial applications (72, 73).

Multiple programs for the surveillance of microorganisms and antimicrobial resistance genes (ARGs) are now operating worldwide. In the United States, the Centers for Disease Control and Prevention (CDC) has led the National Wastewater Surveillance System (NWSS) (74) since 2020, monitoring SARS-CoV-2, influenza viruses, respiratory syncytial virus (RSV), mpox virus (MPXV), and ARGs across more than 1,500 sentinel sites, covering approximately 45% of the U.S. population.

In Europe, the Sewage Sentinel System (EU4S) (75), coordinated by the European Commission, monitors SARS-CoV-2, RSV, influenza viruses, and antimicrobial resistance (AMR) across 11 EU member states, with more than 1 million measurements collected. Furthermore, in Asia, the Mekong Basin Disease Surveillance (MBDS) (76), network, involving Cambodia, China, Laos, Myanmar, Thailand, and Vietnam, implements cross-border surveillance efforts, including wastewater monitoring for priority infectious diseases such as dengue, malaria, influenza, cholera, and tuberculosis. Likewise, in Oceania, Australia launched the National Wastewater Surveillance Program (77) in 2025 to monitor SARS-CoV-2, influenza viruses, RSV, poliovirus, mpox virus, and Japanese encephalitis virus across the entire country.

However, while comprehensive surveillance systems have been established in many high-income countries, efforts in Latin America, Africa, and other low- and middle-income regions remain largely limited to pilot projects or short-term studies, often constrained by funding, infrastructure, and technical capacity (71). Given the proven benefits of environmental surveillance for both pandemic containment and AMR mitigation, there is an urgent need to promote its global expansion and ensure that all countries, regardless of income level, have access to the tools and capacities required to implement sustainable, integrated surveillance programs.

7 Conclusion

• Molecular epidemiology has significantly advanced the detection and monitoring of waterborne pathogens and AMR in aquatic environments. However, several challenges remain, particularly in resource-limited settings.

• Robust and representative sampling strategies are essential to avoid false negatives and ensure nucleic acid integrity.

• Among molecular techniques, PCR and qPCR remain the most widely used due to their sensitivity, reproducibility, and relative accessibility. dPCR offers improved quantification accuracy but still requires cost reduction, while the LAMP technique shows promising field applicability and low cost, though it needs further optimization.

• Genomic and metagenomic approaches provide valuable information on pathogen diversity, evolutionary traits, and ARGs, which are highly important for epidemiological programs. However, their high costs and limited infrastructure in LMICs restrict their widespread use, especially in non-clinical samples.

• Wastewater-based surveillance and epidemiology are valuable early warning tools for outbreaks, but their integration into public health systems remains limited, especially in LMICs. Expanding their impact requires affordable, validated diagnostics, regional collaboration, and alignment with One Health strategies, ensuring that environmental data can be translated into timely public health interventions and to improve preparedness, especially in vulnerable regions.

Author contributions

BM-G: Conceptualization, Formal analysis, Investigation, Visualization, Writing – original draft, Writing – review & editing. JM-M: Investigation, Writing – original draft, Writing – review & editing. CU-S: Investigation, Writing – original draft, Writing – review & editing. KB: Investigation, Writing – original draft, Writing – review & editing. LC: Conceptualization, Funding acquisition, Investigation, Writing – original draft, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This study was funded by the National Institutes of Health Fogarty International Center (grant number D43TW011403) for the project entitled “International Training Program in Environmental Health over the Lifespan” (Claudio L and van Wendel de Joode B, PIs), a grant awarded to the Icahn School of Medicine at Mount Sinai and Universidad Nacional, Costa Rica. Additional support was provided by the Universidad de Costa Rica through researcher salaries and institutional infrastructure for the development of this project.

Acknowledgments

The authors also thank the training and academic support provided through this program.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The authors declare that Gen AI was used in the creation of this manuscript. The authors verify and take full responsibility for the use of generative AI in the preparation of this manuscript. Generative AI was used solely to improve the clarity and grammar of the English language. All content, including scientific concepts, data interpretation, and conclusions, was developed entirely by the authors. No AI-generated content was used to generate original ideas, analyses, or references.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1. World Health Organization (WHO) and United Nations Children’s Fund (UNICEF). Progress on household drinking water, sanitation and hygiene 2000-2020_five years into SDGs. Geneva: WHO and UNICEF (2022).

Google Scholar

2. Persson, L, Carney Almroth, BM, Collins, CD, Cornell, S, de Wit, CA, Diamond, ML, et al. Outside the safe operating space of the planetary boundary for novel entities. Environ Sci Technol. (2022) 56:1510–21. doi: 10.1021/acs.est.1c04158

PubMed Abstract | Crossref Full Text | Google Scholar

3. La Farré, M, Pérez, S, Kantiani, L, and Barceló, D. Fate and toxicity of emerging pollutants, their metabolites and transformation products in the aquatic environment. TrAC Trends Anal Chem. (2008) 27:991–1007. doi: 10.1016/j.trac.2008.09.010

Crossref Full Text | Google Scholar

4. Nnadozie, CF, and Odume, ON. Freshwater environments as reservoirs of antibiotic resistant Bacteria and their role in the dissemination of antibiotic resistance genes. Environ Pollut. (2019) 254:113067. doi: 10.1016/j.envpol.2019.113067

PubMed Abstract | Crossref Full Text | Google Scholar

5. Khodaparast, M, Sharley, D, Marshall, S, and Beddoe, T. Advances in point-of-care and molecular techniques to detect waterborne pathogens. NPJ Clean Water. (2024) 7:1–21. doi: 10.1038/s41545-024-00368-9

PubMed Abstract | Crossref Full Text | Google Scholar

6. López, A, Rodríguez-Chueca, J, Mosteo, R, Gómez, J, Rubio, E, Goñi, P, et al. How does urban wastewater treatment affect the microbial quality of treated wastewater? Process Saf Environ Prot. (2019) 130:22–30. doi: 10.1016/j.psep.2019.07.016

Crossref Full Text | Google Scholar

7. Wang, J, Chu, L, Wojnárovits, L, and Takács, E. Occurrence and fate of antibiotics, antibiotic resistant genes (ARGs) and antibiotic resistant Bacteria (ARB) in municipal wastewater treatment plant: an overview. Sci Total Environ. (2020) 744:140997. doi: 10.1016/j.scitotenv.2020.140997

PubMed Abstract | Crossref Full Text | Google Scholar

8. Manaia, CM, Rocha, J, Scaccia, N, Marano, R, Radu, E, Biancullo, F, et al. Antibiotic resistance in wastewater treatment plants: tackling the black box. Environ Int. (2018) 115:312–24. doi: 10.1016/j.envint.2018.03.044

PubMed Abstract | Crossref Full Text | Google Scholar

9. Mao, K, Zhang, K, Du, W, Ali, W, Feng, X, and Zhang, H. The potential of wastewater-based epidemiology as surveillance and early warning of infectious disease outbreaks. Curr Opin Environ Sci Health. (2020) 17:1–7. doi: 10.1016/j.coesh.2020.04.006

PubMed Abstract | Crossref Full Text | Google Scholar

10. Haramoto, E, Kitajima, M, Hata, A, Torrey, JR, Masago, Y, Sano, D, et al. A review on recent Progress in the detection methods and prevalence of human enteric viruses in water. Water Res. (2018) 135:168–86. doi: 10.1016/j.watres.2018.02.004

PubMed Abstract | Crossref Full Text | Google Scholar

11. Farkas, K, Hassard, F, McDonald, JE, Malham, SK, and Jones, DL. Evaluation of molecular methods for the detection and quantification of pathogen-derived nucleic acids in sediment. Front Microbiol. (2017) 8:53. doi: 10.3389/fmicb.2017.00053

PubMed Abstract | Crossref Full Text | Google Scholar

12. Ramírez-Castillo, F, Loera-Muro, A, Jacques, M, Garneau, P, Avelar-González, F, Harel, J, et al. Waterborne pathogens: detection methods and challenges. Pathogens. (2015) 4:307–34. doi: 10.3390/pathogens4020307

PubMed Abstract | Crossref Full Text | Google Scholar

13. Anvari, M, Gharib, A, Abolhasani, M, Azari-Yaam, A, Gharalari, F, Safavi, M, et al. Pre-analytical practices in the molecular diagnostic tests, a concise review. Iran J Pathol. (2021) 16:1–19. doi: 10.30699/ijp.2020.124315.2357

PubMed Abstract | Crossref Full Text | Google Scholar

14. Ahmed, W, Angel, N, Edson, J, Bibby, K, Bivins, A, O’Brien, JW, et al. First confirmed detection of SARS-CoV-2 in untreated wastewater in Australia: a proof of concept for the wastewater surveillance of COVID-19 in the community. Sci Total Environ. (2020) 728:138764–4. doi: 10.1016/j.scitotenv.2020.138764

Crossref Full Text | Google Scholar

15. Kitajima, M, Sassi, HP, and Torrey, JR. Pepper mild mottle virus as a water quality indicator. NPJ Clean Water. (2018) 1:19–9. doi: 10.1038/s41545-018-0019-5

Crossref Full Text | Google Scholar

16. Bergion, V, Sokolova, E, Åström, J, Lindhe, A, Sörén, K, and Rosén, L. Hydrological modelling in a drinking water catchment area as a means of evaluating pathogen risk reduction. J Hydrol. (2017) 544:74–85. doi: 10.1016/j.jhydrol.2016.11.011

Crossref Full Text | Google Scholar

17. Lapen, DR, Schmidt, PJ, Thomas, JL, Edge, TA, Flemming, C, Keithlin, J, et al. Towards a more accurate quantitative assessment of seasonal Cryptosporidium infection risks in surface waters using species and genotype information. Water Res. (2016) 105:625–37. doi: 10.1016/j.watres.2016.08.023

PubMed Abstract | Crossref Full Text | Google Scholar

18. Quon, H, and Jiang, S. Quantitative microbial risk assessment of antibiotic-resistant E. coli, Legionella pneumophila, and mycobacteria in nonpotable wastewater reuse applications. Environ Sci Technol. (2024) 58:12888–98. doi: 10.1021/acs.est.4c01690

PubMed Abstract | Crossref Full Text | Google Scholar

19. Matrajt, G, Naughton, B, Bandyopadhyay, AS, and Meschke, JS. A review of the Most commonly used methods for sample collection in environmental surveillance of poliovirus. Clin Infect Dis. (2018) 67:S90–7. doi: 10.1093/cid/ciy638

PubMed Abstract | Crossref Full Text | Google Scholar

20. Cassidy, R, and Jordan, P. Limitations of instantaneous water quality sampling in surface-water catchments: comparison with near-continuous phosphorus time-series data. J Hydrol. (2011) 405:182–93. doi: 10.1016/j.jhydrol.2011.05.020

Crossref Full Text | Google Scholar

21. Besmer, MD, Hammes, F, Sigrist, JA, and Ort, C. Evaluating monitoring strategies to detect precipitation-induced microbial contamination events in Karstic Springs used for drinking water. Front Microbiol. (2017) 8:2229. doi: 10.3389/fmicb.2017.02229

PubMed Abstract | Crossref Full Text | Google Scholar

22. Piniewski, M, Marcinkowski, P, Koskiaho, J, and Tattari, S. The effect of sampling frequency and strategy on water quality modelling driven by high-frequency monitoring data in a boreal catchment. J Hydrol. (2019) 579:124186–6. doi: 10.1016/j.jhydrol.2019.124186

Crossref Full Text | Google Scholar

23. Pillai, SD, and Rambo, CH. Water Quality Assessment: Routine Techniques for Monitoring Bacterial and Viral Contaminants In: Batt CA, Tortorello ML, editors. Encyclopedia of Food Microbiology. 2nd ed. London: Academic Press (Elsevier) (2014). 766–772. doi: 10.1016/B978-0-12-384730-0.00352-9

Crossref Full Text | Google Scholar

24. Anttila, S, Ketola, M, Vakkilainen, K, and Kairesalo, T. Assessing temporal representativeness of water quality monitoring data. J Environ Monit. (2012) 14:589–95. doi: 10.1039/C2EM10768F

Crossref Full Text | Google Scholar

25. Cassidy, R, Jordan, P, Bechmann, M, Kronvang, B, Kyllmar, K, and Shore, M. Assessments of composite and discrete sampling approaches for water quality monitoring. Water Resour Manag. (2018) 32:3103–18. doi: 10.1007/s11269-018-1978-5

Crossref Full Text | Google Scholar

26. Gogoi, G, Singh, SD, Kalyan, E, Koch, D, Gogoi, P, Kshattry, S, et al. An interpretative review of the wastewater-based surveillance of the SARS-CoV-2: where do we stand on its presence and concern? Front Microbiol. (2024) 15:8100. doi: 10.3389/fmicb.2024.1338100

PubMed Abstract | Crossref Full Text | Google Scholar

27. WHO. Guidelines for safe recreational water environments v.2: swimming pools and similar environments. Geneva: World Health Organization (2006). 118 p.

Google Scholar

28. American Public Health Association. Standard methods for the examination of water and wastewater. 23rd ed. Washington, DC: American Public Health Association (2017).

Google Scholar

29. Wu, W-K, Chen, C-C, Panyod, S, Chen, R-A, Wu, M-S, Sheen, L-Y, et al. Optimization of Fecal sample processing for microbiome study — the journey from bathroom to bench. J Formos Med Assoc. (2019) 118:545–55. doi: 10.1016/j.jfma.2018.02.005

PubMed Abstract | Crossref Full Text | Google Scholar

30. Smith, CM, and Hill, VR. Dead-end hollow-Fiber ultrafiltration for recovery of diverse microbes from water. Appl Environ Microbiol. (2009) 75:5284–9. doi: 10.1128/AEM.00456-09

PubMed Abstract | Crossref Full Text | Google Scholar

31. Bofill-Mas, S, and Rusiñol, M. Recent trends on methods for the concentration of viruses from water samples. Curr Opin Environ Sci Health. (2020) 16:7–13. doi: 10.1016/j.coesh.2020.01.006

Crossref Full Text | Google Scholar

32. Bleotu, C, Matei, L, Dragu, LD, Necula, LG, Pitica, IM, Chivu-Economescu, M, et al. Viruses in wastewater—a concern for public health and the environment. Microorganisms. (2024) 12:1430–08. doi: 10.3390/microorganisms12071430

PubMed Abstract | Crossref Full Text | Google Scholar

33. Liu, P, Hill, VR, Hahn, D, Johnson, TB, Pan, Y, Jothikumar, N, et al. Hollow-Fiber ultrafiltration for simultaneous recovery of viruses, Bacteria and parasites from reclaimed water. J Microbiol Methods. (2012) 88:155–61. doi: 10.1016/j.mimet.2011.11.007

PubMed Abstract | Crossref Full Text | Google Scholar

34. Diamanti, C, Nousis, L, Bozidis, P, Koureas, M, Kyritsi, M, Markozannes, G, et al. Wastewater surveillance of SARS-CoV-2: a comparison of two concentration methods. Viruses. (2024) 16:1398–8. doi: 10.3390/v16091398

PubMed Abstract | Crossref Full Text | Google Scholar

35. Carmo dos Santos, M, Cerqueira Silva, AC, dos Reis Teixeira, C, Pinheiro Macedo Prazeres, F, Fernandes dos Santos, R, de Araújo Rolo, C, et al. Wastewater surveillance for viral pathogens: a tool for public health. Heliyon. (2024) 10:e33873–3. doi: 10.1016/j.heliyon.2024.e33873

PubMed Abstract | Crossref Full Text | Google Scholar

36. Pons Royo, M d C, and Jungbauer, A. Polyethylene glycol precipitation: fundamentals and recent advances. Prep Biochem Biotechnol. (2025) 55:935–54. doi: 10.1080/10826068.2025.2470220

PubMed Abstract | Crossref Full Text | Google Scholar

37. Farmer-Diaz, K, Matthew-Bernard, M, Cheetham, S, Mitchell, K, Macpherson, CNL, and Ramos-Nino, ME. Optimized aluminum hydroxide adsorption–precipitation for improved viral detection in wastewater. Int J Environ Res Public Health. (2025) 22:148–8. doi: 10.3390/ijerph22020148

PubMed Abstract | Crossref Full Text | Google Scholar

38. Ikner, LA, Gerba, CP, and Bright, KR. Concentration and recovery of viruses from water: a comprehensive review. Food Environ. Virol. (2012) 4:41–67. doi: 10.1007/s12560-012-9080-2

PubMed Abstract | Crossref Full Text | Google Scholar

39. Wang, Y, Hammes, F, Düggelin, M, and Egli, T. Influence of size, shape, and flexibility on bacterial passage through micropore membrane filters. Environ Sci Technol. (2008) 42:6749–54. doi: 10.1021/es800720n

PubMed Abstract | Crossref Full Text | Google Scholar

40. Lebleu, N, Roques, C, Aimar, P, and Causserand, C. Role of the cell-wall structure in the retention of bacteria by microfiltration membranes. J Membr Sci. (2009) 326:178–85. doi: 10.1016/j.memsci.2008.09.049

Crossref Full Text | Google Scholar

41. Environmental Protection Agency, U.S. Office of Water. Sampling and consideration of variability (temporal and spatial) for monitoring of recreational waters. Washington, DC: U.S. Environmental Protection Agency. (2010). EPA-823-R-10-005.

Google Scholar

42. National Academies of Sciences, E., and Medicine. Increasing the utility of wastewater-based disease surveillance for public health action. Washington, D.C.: National Academies Press (2024).

Google Scholar

43. Osathanunkul, M, and Suwannapoom, C. A comparative study on eDNA-based detection of Siamese bat catfish (Oreoglanis siamensis) in wet and dry conditions. Sci Rep. (2024) 14:8885–5. doi: 10.1038/s41598-024-58752-x

PubMed Abstract | Crossref Full Text | Google Scholar

44. Harper, LR, Buxton, AS, Rees, HC, Bruce, K, Brys, R, Halfmaerten, D, et al. Prospects and challenges of environmental DNA (eDNA) monitoring in freshwater ponds. Hydrobiologia. (2019) 826:25–41. doi: 10.1007/s10750-018-3750-5

Crossref Full Text | Google Scholar

45. Girones, R, Ferrús, MA, Alonso, JL, Rodriguez-Manzano, J, Calgua, B, de Abreu Corrêa, A, et al. Molecular detection of pathogens in water – the pros and cons of molecular techniques. Water Res. (2010) 44:4325–39. doi: 10.1016/j.watres.2010.06.030

PubMed Abstract | Crossref Full Text | Google Scholar

46. Cangelosi, GA, and Meschke, JS. Dead or alive: molecular assessment of microbial viability. Appl Environ Microbiol. (2014) 80:5884–91. doi: 10.1128/AEM.01763-14

PubMed Abstract | Crossref Full Text | Google Scholar

47. Aliu Olalekan, O, Olaboye, JA, Maha, CC, Kolawole, TO, and Abdul, S. Revolutionizing infectious disease management in low-resource settings: the impact of rapid diagnostic technologies and portable devices. Int J Appl Res Soc Sci. (2024) 6:1417–32. doi: 10.51594/ijarss.v6i7.1332

Crossref Full Text | Google Scholar

48. Zhang, S, Li, X, Wu, J, Coin, L, O’Brien, J, Hai, F, et al. Molecular methods for pathogenic bacteria detection and recent advances in wastewater analysis. Water. (2021) 13:3551–1. doi: 10.3390/w13243551

Crossref Full Text | Google Scholar

49. Masachessi, G, Castro, G, Cachi, AM, Marinzalda, M d l Á, Liendo, M, Pisano, MB, et al. Wastewater based epidemiology as a silent sentinel of the trend of SARS-CoV-2 circulation in the community in Central Argentina. Water Res. (2022) 219:118541. doi: 10.1016/j.watres.2022.118541

Crossref Full Text | Google Scholar

50. Chen, C, Wang, Y, Kaur, G, Adiga, A, Espinoza, B, Venkatramanan, S, et al. Wastewater-based epidemiology for COVID-19 surveillance and beyond: a survey. Epidemics. (2024) 49:100793. doi: 10.1016/j.epidem.2024.100793

PubMed Abstract | Crossref Full Text | Google Scholar

51. Tiwari, A, Ahmed, W, Oikarinen, S, Sherchan, SP, Heikinheimo, A, Jiang, G, et al. Application of digital PCR for public health-related water quality monitoring. Sci Total Environ. (2022) 837:155663. doi: 10.1016/j.scitotenv.2022.155663

PubMed Abstract | Crossref Full Text | Google Scholar

52. Prajapati, B, Rathore, D, Joshi, C, and Joshi, M. Digital PCR: a partitioning-based application for detection and surveillance of SARS-CoV-2 from sewage samples In: L Domingues, editor. PCR: Methods and protocols. New York, NY: Springer US (2023). 1–16.

Google Scholar

53. Garg, N, Ahmad, FJ, and Kar, S. Recent advances in loop-mediated isothermal amplification (LAMP) for rapid and efficient detection of pathogens. Curr Res Microb Sci. (2022) 3:100120. doi: 10.1016/j.crmicr.2022.100120

PubMed Abstract | Crossref Full Text | Google Scholar

54. Soroka, M, Wasowicz, B, and Rymaszewska, A. Loop-mediated isothermal amplification (LAMP): the better sibling of PCR? Cells. (2021) 10:1931. doi: 10.3390/cells10081931

PubMed Abstract | Crossref Full Text | Google Scholar

55. Wong, Y-P, Othman, S, Lau, Y-L, Radu, S, and Chee, H-Y. Loop-mediated isothermal amplification (LAMP): a versatile technique for detection of micro-organisms. J Appl Microbiol. (2018) 124:626–43. doi: 10.1111/jam.13647

Crossref Full Text | Google Scholar

56. Jessen, R, Nielsen, L, Larsen, NB, Cohen, AS, Gunalan, V, Marving, E, et al. A RT-qPCR system using a degenerate probe for specific identification and differentiation of SARS-CoV-2 omicron (B.1.1.529) variants of concern. PLoS One. (2022) 17:e0274889. doi: 10.1371/journal.pone.0274889

PubMed Abstract | Crossref Full Text | Google Scholar

57. Mangini, AT, Prandi, BA, Violet-Lozano, L, Cunha, PF, Jarenkow, A, Campos, AAS, et al. Detection of SARS-CoV-2 variants related mutations in wastewater using RT-qPCR and variant-specific probes in Porto Alegre, southern Brazil. J Water Health. (2024) 22:1774–80. doi: 10.2166/wh.2024.042

Crossref Full Text | Google Scholar

58. Rupprom, K, Chavalitshewinkoon-Petmitr, P, Diraphat, P, and Kittigul, L. Evaluation of real-time RT-PCR assays for detection and quantification of norovirus genogroups I and II. Virol Sin. (2017) 32:139–46. doi: 10.1007/s12250-016-3863-9

PubMed Abstract | Crossref Full Text | Google Scholar

59. Gardy, JL, and Loman, NJ. Towards a genomics-informed, real-time, global pathogen surveillance system. Nat Rev Genet. (2018) 19:9–20. doi: 10.1038/nrg.2017.88

PubMed Abstract | Crossref Full Text | Google Scholar

60. Inzaule, SC, Tessema, SK, Kebede, Y, Ogwell Ouma, AE, and Nkengasong, JN. Genomic-informed pathogen surveillance in Africa: opportunities and challenges. Lancet Infect Dis. (2021) 21:e281–9. doi: 10.1016/S1473-3099(20)30939-7

PubMed Abstract | Crossref Full Text | Google Scholar

61. Yek, C, Pacheco, AR, Vanaerschot, M, Bohl, JA, Fahsbender, E, Aranda-Díaz, A, et al. Metagenomic pathogen sequencing in resource-scarce settings: lessons learned and the road ahead. Front Epidemiol. (2022) 2:6695. doi: 10.3389/fepid.2022.926695

PubMed Abstract | Crossref Full Text | Google Scholar

62. Oon, Y-L, Oon, Y-S, Ayaz, M, Deng, M, Li, L, and Song, K. Waterborne pathogens detection technologies: advances, challenges, and future perspectives. Front Microbiol. (2023) 14:6923. doi: 10.3389/fmicb.2023.1286923

PubMed Abstract | Crossref Full Text | Google Scholar

63. Ko, KKK, Chng, KR, and Nagarajan, N. Metagenomics-enabled microbial surveillance. Nat Microbiol. (2022) 7:486–96. doi: 10.1038/s41564-022-01089-w

PubMed Abstract | Crossref Full Text | Google Scholar

64. Shen, J, McFarland, AG, Young, VB, Hayden, MK, and Hartmann, EM. Toward accurate and robust environmental surveillance using metagenomics. Front Genet. (2021) 12:111. doi: 10.3389/fgene.2021.600111

PubMed Abstract | Crossref Full Text | Google Scholar

65. Arena-Ortiz, ML, Sánchez-Rodríguez, EC, Apodaca-Hernández, JE, Ortiz-Alcántara, JM, Ríos-Contreras, K, and Chiappa-Carrara, X. DNA microarrays to identify etiological agents, as sensors of environmental wellbeing. Front Bioeng Biotechnol. (2023) 11:5976. doi: 10.3389/fbioe.2023.1085976

PubMed Abstract | Crossref Full Text | Google Scholar

66. Cancela, F, Ramos, N, Smyth, DS, Etchebehere, C, Berois, M, Rodríguez, J, et al. Wastewater surveillance of SARS-CoV-2 genomic populations on a country-wide scale through targeted sequencing. PLoS One. (2023) 18:e0284483. doi: 10.1371/journal.pone.0284483

PubMed Abstract | Crossref Full Text | Google Scholar

67. Cancela, F, Lizasoain, A, Panzera, Y, Fernández-López, E, Lozano, J, Calleros, L, et al. Targeted enrichment sequencing utilizing a respiratory pathogen panel for genomic wastewater-based viral epidemiology in Uruguay. Food Environ Virol. (2025) 17:14. doi: 10.1007/s12560-024-09629-9

PubMed Abstract | Crossref Full Text | Google Scholar

68. Kumar, S, Nehra, M, Mehta, J, Dilbaghi, N, Marrazza, G, and Kaushik, A. Point-of-care strategies for detection of waterborne pathogens. Sensors. (2019) 19:4476. doi: 10.3390/s19204476

PubMed Abstract | Crossref Full Text | Google Scholar

69. Zarei, M. Portable biosensing devices for point-of-care diagnostics: recent developments and applications. TrAC Trends Anal Chem. (2017) 91:26–41. doi: 10.1016/j.trac.2017.04.001

Crossref Full Text | Google Scholar

70. Wastewater and Environmental Surveillance. Available online at: https://www.who.int/teams/environment-climate-change-and-health/water-sanitation-and-health/sanitation-safety/wastewater (Accessed 30 May, 2025).

Google Scholar

71. Environment, U.N. Bracing for superbugs: strengthening environmental action in the one health response to antimicrobial resistance | UNEP - UN environment programme. Available online at: https://www.unep.org/resources/superbugs/environmental-action (Accessed 03 June, 2025).

Google Scholar

72. WHO. Global Antimicrobial Resistance and use Surveillance System (GLASS). Available online at: https://www.who.int/initiatives/glass (Accessed 03 June, 2025).

Google Scholar

73. Food and Agriculture Organization of the United Nations; World Organisation for Animal Health; World Health Organization; United Nations Environment Programme. Joint Tripartite (FAO, OIE, WHO) and UNEP Statement – Tripartite and UNEP support OHHLEP’s definition of “One Health”. Geneva, Switzerland: FAO / WHO / OIE / UNEP. (2021).

Google Scholar

74. CDC. About CDC’S National Wastewater Surveillance System (NWSS). Available online at: https://www.cdc.gov/nwss/about.html (Accessed 03 June, 2025).

Google Scholar

75. EU4S Available online at: https://wastewater-observatory.jrc.ec.europa.eu/#/ (Accessed 03 June, 2025).

Google Scholar

76. Mekong Basin Disease Surveillance Biosafety. Available online at: https://www.mbdsbiosafety.net/ (Accessed 03 June, 2025).

Google Scholar

77. Control, A.C. for D. National Wastewater Surveillance Program. Available online at: https://www.cdc.gov.au/topics/communicable-diseases-surveillance/national-wastewater-surveillance-program (Accessed 03 June, 2025).

Google Scholar

Keywords: ARGS, public health surveillance, resource-limited settings, PCR-based detection, waterborne pathogens, environmental monitoring

Citation: ​Mendoza-Guido B, ​Montiel-Mora JR, Ureña-Salazar C, Barrantes K and Chacón L (2025) Molecular epidemiology of aquatic environments: challenges from sampling to implementation of surveillance programs. Front. Public Health. 13:1652535. doi: 10.3389/fpubh.2025.1652535

Received: 23 June 2025; Accepted: 17 September 2025;
Published: 01 October 2025.

Edited by:

David Sue, Centers for Disease Control and Prevention (CDC), United States

Reviewed by:

Bhupinder Kaur, Akal Degree College Mastuana, India

Copyright © 2025 Mendoza-Guido, Montiel-Mora, Ureña-Salazar, Barrantes and Chacón. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Luz Chacón, bHV6LmNoYWNvbkB1Y3IuYWMuY3I=

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.