Systematic review on the laboratory methodology for conducting wastewater and environmental surveillance (WES) for Salmonella

Systematic review on the laboratory methodology for conducting wastewater and environmental surveillance (WES) for Salmonella | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Systematic review on the laboratory methodology for conducting wastewater and environmental surveillance (WES) for Salmonella Lucky Sangal, Vishesh Sood, Karin Haar, Takana Silubonde Moyana, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7998434/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Salmonella infections continue to pose a significant public health challenge in low- and middle-income countries (LMICs), particularly in Southeast Asia. Wastewater and environmental surveillance (WES) offers a promising approach for supplementing clinical and field surveillance methods for early detection and monitoring. This systematic review aimed to evaluate laboratory methodologies for detecting Salmonella spp. in wastewater and contaminated surface waters. Following the PRISMA 2020 guidelines, PubMed, EMBASE, and Web of Science (1980–2024) were searched for studies that described sampling and laboratory methods for detecting Salmonella in environmental water. Data extraction and quality assessment used standardized templates. Out of 2,007 records, 94 studies met the inclusion criteria. Methodological heterogeneity was high, with grab sampling and Moore swabs predominating; Salmonella detection methods included culture, PCR, and genomic sequencing. Fewer than 30% of studies reported comprehensive quality control. Based on the systematic review, a need for standardized, context-adapted protocol was identified to enhance WES utility for Salmonella surveillance in LMICs. Salmonella Typhi wastewater surveillance laboratory methodology South-East Asia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Wastewater and environmental surveillance (WES) is well-established for poliovirus surveillance as a core part of the Global Polio Eradication Initiative (World Health Organization 2003 ; GPEI 2015 ; GPEI 2023 ; GPEI 2025 ), and gained further prominence during the COVID-19 pandemic as a valuable tool for understanding the disease burden in communities (Levy et al. 2023 ) The success of WES in COVID-19 has sparked interest in its use to monitor other pathogens of public health concern (Grassly et al. 2025 ). Prioritization of pathogens for WES remains a fundamental question for optimizing resource utilization and ensuring operational flexibility and adaptability in the event of outbreaks caused by new pathogens (Tiwari et al. 2024 ; Toro et al. 2024 ; World Health Organization 2024a ). Proposed prioritization frameworks emphasize several key factors for successful WES adaptation, including the public health significance of the pathogen, the usefulness of WES data for public health actions, and the analytical feasibility of conducting WES (Toro et al. 2024 ; World Health Organization 2024a ). Prioritizing a pathogen within this framework helps align WES efforts with broader public health goals. Salmonella infections, particularly with typhoid and paratyphoid serovars, continue to pose a substantial burden on global public health systems, and remain a significant challenge in low- and middle-income countries (LMICs), including within the World Health Organization's South-East Asia Region (SEAR) (Kim et al. 2019 ; Wang et al. 2024 ). Non-typhoid and non- paratyphoid Salmonella serovars. infect both humans and various animals, making them a significant concern for zoonotic transmission and for both animal husbandry and the food industry (Hoelzer et al. 2011 ; Ferrari et al. 2019 ). Asymptomatic carriers and subclinical infections play a key role in maintaining the transmission chain of Salmonella infections (Khanam et al. 2021 ; Lu et al. 2024 ). Therefore, accurately determining the true prevalence of Salmonella -related diseases requires supplementing clinical surveillance with serosurveys or contact tracing during outbreaks (Cao et al. 2021 ; Uwanibe et al. 2023 ). However, current methods for additional surveillance have limited sensitivity. For instance, the clinical diagnosis of typhoid and paratyphoid often relies on non-specific Widal tests or blood cultures, both of which have low sensitivity due to suboptimal sampling times post-incubation period (Andualem et al. 2014 ; Mawazo et al. 2019 ). Despite the World Health Organization not recommending the Widal test, it remains widely used in clinical practice in our region. In addition to its limited sensitivity, the Widal test is prone to cross-reactivity with other pathogens, further reducing its diagnostic specificity. Additionally, estimating HlyE IgG antibodies using ELISA is the preferred method for serosurveys on typhoid and paratyphoid prevalence; however, HlyE antibodies can exhibit cross-reactivity, as many other bacteria also express HlyE (Kumar et al. 2020 ; Aiemjoy et al. 2022 ). As a result, there is a need to establish supplementary tools to accurately estimate the true prevalence of the infections caused by Salmonella Typhi and Paratyphi. Since Salmonella is present in wastewater due to shedding in the feces of both symptomatic and asymptomatic individuals, depending on the stage of infection, WES has proven effective in evaluating its community burden in endemic countries, complementing existing clinical surveillance and serosurveys (Yanagimoto et al. 2020 ; Uzzell et al. 2024a ; Abraham et al. 2025 ). Besides assessing community-level disease burdens, WES can also generate data on circulating strains and antimicrobial resistance—provided the bacteria can be cultured—which directly informs vaccination and antimicrobial resistance (AMR) strategies, offering significant public health benefits for Salmonella monitoring (Yan et al. 2018 ; Diemert and Yan 2020 ). Therefore, the public health importance of Salmonella and the utility of WES make it a priority pathogen for WES implementation (World Health Organization 2024b ). The primary challenge of Salmonella WES lies in analytical feasibility, due to the heterogeneous and variable factors outside the laboratory that affect sample collection and quality. Unlike high-income countries with centralized and closed sewage systems, most Salmonella Typhi and Paratyphi-endemic countries, particularly in the SEAR region (Abraham et al. 2025 ; Jahan et al. 2025 ; Oktaria et al. 2025 ), frequently rely on decentralized, informal, or mixed drainage networks, including open drains, septic tanks, and combined sewer-stormwater systems, which are often poorly maintained and vulnerable to contamination during monsoons (Sotelo et al. 2019 ; Nasim et al. 2022 ). Thus, the pre-examination factors such as variability in infrastructure, flow dynamics, and ambient conditions complicate sample collection and pathogen recovery and demands context-specific adaptations to sampling and testing protocols. Laboratory capacity constraints, including cold chain logistics and molecular diagnostics standardization and result interpretations, further limit the applicability of WES protocols (Jahan et al. 2025 ; Oktaria et al. 2025 ; Owusu et al. 2025 ). Inherent variability in sampling site characteristics, public health goals, and laboratory capacity underscores the urgent need to develop harmonized methodologies that are scientifically sound, operationally practical, and adaptable to diverse infrastructure contexts, particularly in low- and middle-income countries. This systematic review was conducted to evaluate the scientific and operational feasibility of laboratory methods for detecting Salmonella in wastewater, aiming to guide the development of harmonized, context-specific field and laboratory protocols that can support regional public health goals like integrated disease surveillance and inform the deployment of typhoid conjugate vaccine (TCV) in endemic areas and LMIC. The review assessed the completeness of methodological reporting; such as site selection, sample handling, and quality control to identify critical gaps in methodological reporting that might impede reproducibility and scalability. The identified methodologies were further categorized as pathways to match protocol steps with the wastewater sampling and socio-economic status of reporting countries. Finally, the review also identified the primary public health domains that researchers utilize to develop a framework for aligning surveillance objectives with laboratory capacities and infrastructure realities. Methods Study design. This qualitative systematic review was designed and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (Page et al. 2021 ). A detailed protocol outlining the objectives, eligibility criteria, and methodological approach was developed before the initiation of the review and registered with the International Prospective Register of Systematic Reviews (PROSPERO) on August 5, 2024 (registration ID: CRD42024573052). Search Strategy. A comprehensive literature search was conducted to identify peer-reviewed studies describing laboratory methodologies for the detection or isolation of Typhoid Salmonella serovar. The initial search was conducted on September 10, 2024, using three major scientific databases: PubMed, EMBASE, and Web of Science. It was updated on May 31, 2025, to include recent publications. The specific combination of databases was chosen because it achieves a 90–95% recall rate in over 80% of reported systematic reviews (Bramer et al. 2017 ). The search strategy was designed to retrieve studies relevant to Salmonella Typhi surveillance in environmental matrices, with a focus on wastewater and surface water. Briefly, a naïve search was conducted on PubMed after identifying relevant MESH and MAJR terms for WES of Salmonella Typhi. The query used was - " Salmonella "[Mesh] AND ("Wastewater-Based Epidemiological Monitoring" [Mesh] OR "Environmental Monitoring"[Mesh] OR "Sewage/microbiology"[MAJR] OR "Wastewater/microbiology"[MAJR]). The easyPubMed package in R was used to import the naïve search results, which were analyzed with the litsearchr package in R to identify keywords in an unbiased manner (Supplementary Fig. S1 and Supplementary Table T3) (Fantini 2019 ; Grames et al. 2019 ). The identified keywords were used to create PubMed search queries using Boolean operators and wildcard symbols (e.g., *). The search query was optimized to evaluate its sensitivity against a benchmark set of 16 studies on WES for typhoid (Supplementary Table T4). The search terms were refined until all 16 benchmark studies were captured (Supplementary Fig. S2 and Supplementary Table T5). Once 100% sensitivity was achieved for the benchmark studies, the final PubMed query was translated into EMBASE and Web of Science formats using the polyglot application (Kung 2022 ). The final search queries for each database are provided in (Supplementary Table T6). Systematic review process. The final search was conducted in May 2025, and results were imported into the Covidence platform, which automatically removed duplicates. Remaining duplicates were manually reviewed and excluded. Title and abstract screening was then performed, with inclusion and exclusion criteria detailed in Supplementary Table T7. Two authors independently reviewed the studies, resolving conflicts by consensus among all authors. Full texts were retrieved for eligible studies and screened again against the criteria. The Covidence platform was also used to prepare templates for data extraction and methodological quality assessment, as described in Supplementary Tables T8 and T9. Data extraction was performed independently by two authors, with disagreements resolved by consensus. Supplementary Table T1 presents the PRISMA checklist. Data analysis and visualization. A narrative synthesis was conducted due to the variability in study designs, sampling strategies, and laboratory methods. Data visualization involved subgroup analysis based on the methodological areas (e.g., sampling, processing, testing), and patterns were identified across the studies. Additionally, the findings were integrated into a decision tool to help frame public health objectives and methodological choices. Python (version 3.12.7) was used within the Spyder IDE (version 6.0.7) to perform data analysis and visualization, using a collection of specialized libraries. Pandas handled data import and transformation; NumPy supported numerical calculations; and SciPy was used for estimating Jaccard distances and performing chi-squared tests. The silhouette score was calculated with Scikit-learn, while NetworkX enabled network analysis. GeoPandas managed geospatial polygon data for countries, Matplotlib produced standard plots, Seaborn generated heatmaps, and UpSetPlot was used to create UpSet diagrams. Geographical mapping of studies was conducted using cultural raster map shapefiles obtained from Natural Earth and analyzed with the GeoPandas library in Python. Since some studies reported using multiple methods or samples, the methods were identified to aid analysis. To find commonalities among the methods, pathway analysis was performed. During this process, methods were clustered using the Jaccard distance approach based on factors such as the economic status of countries, sample types, and the presence or absence of specific protocol steps. For simplicity, the LMIC classification included all countries from the LIC (low-income countries), LMIC, and UMIC (upper middle-income countries) categories. The samples analyzed included grab samples, trap samples, and composite samples. Protocol steps included processing, culture, biotyping (using biochemicals and other biotyping techniques), serotyping (via the Kauffman-White scheme and PCR), antimicrobial susceptibility testing, genotyping (using molecular assays and ARGs), and genomics methods, including targeted sequencing, whole-genome sequencing, and metagenomics. The methods were grouped using the Jaccard distance metric with average linkage, and the optimal number of clusters was determined through the silhouette score and the elbow method. A Chi-square test of independence was performed using SciPy to evaluate whether different protocol steps occur disproportionately across various pathways. A p-value less than 0.05 was considered statistically significant, indicating that a protocol step influences the clustering. To identify under- or over-representation of a step within a specific pathway, residual analysis was performed, with values greater than 2 indicating a significant association and values less than − 2 indicating no significant association. Study domains were identified based on the titles, keywords, and abstracts of the selected studies. Eight domains were recognized: A) outbreak detection/investigation, B) disease prevalence, C) AMR prevalence, D) mechanisms of AMR, E) wastewater monitoring, F) environmental health, G) One Health, and H) method validation. Some studies encompassed more than one domain. The co-occurrence of domains was calculated using NumPy. To visualize the core domains and their connections to other domains, the co-occurrence matrix was interpreted as an undirected weighted graph with NetworkX. The network comprises nodes representing individual domains, with node size proportional to the number of studies in each domain. Edges indicate co-occurrences between domains, with edge width reflecting the strength of these co-occurrences. Additionally, a force-directed spring layout was used to position strongly related domains closer together. Reporting bias. The review aimed to eliminate bias during both the searching and review stages. To minimize search bias, multiple databases were utilized, and the search string was refined through unbiased keyword selection and by evaluating the search strategy against a set of benchmark studies. For study selection bias, two authors independently reviewed the abstracts during the screening process to determine eligibility for full-text review, and two authors independently conducted the data extraction. Results A total of 2,007 articles were identified across PubMed, Embase, and Web of Science. After removing 686 duplicates, 1,321 records were screened by title and abstract, resulting in 1,143 exclusions. Of the 178 full-text articles assessed, 94 met the eligibility criteria and were included in the review. Studies were excluded for reasons such as incomplete methodology, non-peer-reviewed status, or publication in a language other than English. Included studies were further classified by methodological quality assessment into five categories: excellent (n = 20), robust (n = 22), good (n = 22), fair (n = 22), and low (n = 8). The PRISMA workflow for the systematic review is presented in Fig. 1. The extracted data and quality assessment data are provided as supplementary datasets 1 and 2, respectively. As some studies used multiple samples and multiple methods, the extracted data were further refined to obtain the methods used for each sample in different studies. This resulted in the identification of 102 methods. Table 1 summarizes the sample types and methodology used by the included studies. The selected studies represented a wide geographic distribution across 36 countries, five of them in SEAR countries (Fig. 2a). Most (n = 89) were single-country studies, while five involved multiple countries. According to the World Bank's income classification, the studies spanned two LICs, ten LMICs, eight UMICs, and 16 high-income countries (HICs). The United States of America and India contributed the highest number of studies published after 2020 (Fig. 2b). The included studies employed a range of sampling methods to collect wastewater, each reflecting different operational contexts and surveillance goals (Table 1). Most studies reported using grab sampling (n = 62), which involves collecting a predefined volume of wastewater in a sterile container at a single point in time. This was followed by trap sampling (n = 10), a passive technique in which a receptacle, often a Moore swab, is exposed to flowing wastewater over a set period to capture microorganisms. Composite sampling (n = 9) was also employed, involving autosamplers that collect wastewater at regular intervals over an extended period. Additionally, some studies (n = 7) employed a combination of grab and trap sampling, while five studies did not specify the type of sample collected. One study uniquely used sewage sludge as the sample type. Sample handling after collection was reported in 61 studies (Table 1). Among these, 56 studies described the transportation of samples under cold chain conditions to preserve microbial integrity. Additionally, 52 studies provided details on the time elapsed between sample collection and laboratory processing. Of these, 49 studies processed samples within 24 hours, while three studies reported delays exceeding 24 hours. Sample processing, defined as the steps taken before initiating the microbiological procedure, was also mentioned by various methods (n=74) (Table 1). The methods reported processing using filtration (n=32) to trap microorganisms or remove large debris, centrifugation (n=8) to pellet the microorganisms, dilution or serial dilution (n=5) to reduce inhibitory components, processing of the Moore swab to extract its contents (n=3), using specially designed magnetic particles that bind with bacteria (n=1), or a combination of methods (n=23) to remove debris and inhibitors and trap microorganisms. The laboratory testing could be further resolved in different protocol steps, including bacterial culture, bacterial enumeration, phenotypic characterization of observable bacterial traits (biochemical identification, serotyping, antimicrobial susceptibility, and other methods), genotypic characterization based on bacterial nucleic acid (PCR and other methods), and sequencing (Fig. 3a). Two studies used a novel bacteriophage-based method that employed detecting Salmonella -specific bacteriophages as an indicator for Salmonella . It was noted that the use of different protocol steps is influenced by the central question or hypothesis that these studies aimed to address. Bacterial cultivation was attempted by 70% of the methods (n=71), as shown in Fig. 3a. This step typically involved enrichment in non-selective or selective media, followed by selective culture or isolation of Salmonella sp., using standard culture media. However, not all studies included a culture step as part of their methodology, some relied solely on molecular or alternative detection techniques. Fig. 3b illustrates the combination of culture steps used in different methods (n = 67), excluding four methods that reported using standard methods (ISO 6579, ISO 19250, FDA Bioanalytical manual protocol for Salmonella , and APHA standard method) and one method that exclusively used bacteriophage-specific methods. Among those that performed culture, the most common protocol involved enrichment, selective enrichment, and selective culture (n = 32), followed by selective enrichment and culture (n = 7). Additionally, four methods reported bacterial enumeration using serial dilutions and the most probable number (MPN) method to estimate bacterial load. Fig. 3a also illustrates how different phenotypic characterization assays were primarily used to characterize isolated bacteria based on observable traits, such as growth in specific media, serotype, or antimicrobial susceptibility. The phenotypic methods included biotyping with biochemical media (n = 48), serotyping (n = 26), antimicrobial susceptibility testing (n = 47), and other phenotypic techniques mainly involving phage typing (n = 3), Matrix-Assisted Laser Desorption/Ionization Time-of-Flight, MALDI-TOF (n = 2), and both MALDI-TOF and phage typing ( n=1). Biotyping utilized standard biochemical identification techniques, either manual (n = 32), automated (n = 12), or a combination of both (n = 3). One study did not specify the biochemicals used. For serotyping, most studies employed the Kauffman-White serotyping scheme to characterize Salmonella isolates (n = 24). Two studies reported the use of a PCR-based serotyping scheme. Regarding antimicrobial susceptibility, the majority of studies used the disc diffusion assay (n = 36), followed by broth microdilution (n = 4), automated systems (n = 3), a combination of disc diffusion and automated systems (n = 2), and a combination of disc diffusion and broth microdilution (n = 1). One of the studies also utilized resistance transfer testing to understand the mechanism of AMR gene transfer to a susceptible host. Genotypic characterization of bacterial nucleic acid by molecular assays primarily included variants of PCR (n = 65), as well as other molecular assays, such as pulsed field gel electrophoresis (n = 10), plasmid analysis (n = 2), DNA fingerprinting (n = 1), fluorescence in situ hybridization (n = 1), and sequencing (n = 28). The methods reported included qPCR (n=22), PCR (n=18), molecular assays for antimicrobial resistance genes (ARG) (n=12), and a combination of qPCR and ARG PCR (n=2). Other than that, one study each reported using multiplex PCR, RT-PCR BioFire FilmArray® panel, crystal digital PCR, high-throughput qPCR, PCR with virulence marker PCR, culture PCR with high-throughput qPCR, 16S RNA PCR with PCR, qPCR with high-throughput qPCR, PCR with qPCR, and qPCR with denaturing gradient gel electrophoresis. Most used genomic method was whole genome sequencing, WGS (n=11), followed by untargeted or shotgun metagenomics (n=6), and 16S rDNA targeted sequencing (n=2). One study each reported targeted sequencing, 16S rRNA sequencing, ARG genes sequencing, untargeted metagenomics, long and short read sequencing using Nanopore and Illumina sequencing, 16S rRNA sequencing with WGS, 16S RNA sequencing with metagenomics, and 16S rDNA sequencing with biomarker sequencing. To further understand the methodology of WES for Salmonella sp., the identified methods were further characterized in terms of common pathways from sample collection to testing. In this approach, 87 different methods from 79 studies were employed (Supplementary Table T10). Fifteen methods from 14 studies were excluded from this analysis because no sample was specified, standard procedures were not discussed in detail, bacteriophage surveillance was not included, or methods used for stored isolates were not specified. Based on the clustering, a total of six pathways were identified (Fig. 4a and Supplementary Fig. S3). The details of each method mapped to a pathway are provided in Supplementary Table T10. Eight methods shared a pathway P1, mainly involving a culture step followed by identification using molecular methods, with a slight association with a processing step. Pathway P2 was the most used pathway, with 43 methods. P2 has a strong association with the processing step, culture, biotyping of isolates, and antimicrobial susceptibility testing. The pathway was mildly associated with molecular assays, with low association to other steps. P3 was shared by seven methods and was associated with culture, biotyping, serotyping, and AST. Seven studies shared pathway P4, which was strongly associated with a processing step and the use of molecular assays for characterization. P4 also had a moderate association with culture and genomics methods. Pathway P5 was the second most used pathway, with 15 methods, primarily involving molecular characterization after a processing step. Lastly, pathway P6 was shared by seven methods and included a processing step and testing using a genomics method. Among the six identified methodological pathways, statistical analysis revealed that all protocol steps, except genotyping for ARG were significantly associated with specific pathways (p < 0.01, Supplementary Fig. S4). This indicates that most steps showed distinct patterns of inclusion across the pathways. Post-hoc residual analysis further confirmed that all steps, except genotyping for ARG, exhibited differential usage across pathways, supporting the robustness of the clustering approach (Supplementary Fig. S5). Fig. 4b illustrates the distribution of methodological pathways across sample types in LMIC and HIC settings. Both LMICs and HICs studies commonly employed pathways P2, P5, and P6 for grab samples. Pathway P2 was used at similar rates in both income groups, while P5 was more prevalent in LMICs and P6 in HICs settings. In LMICs, P5 was also applied to composite and trap samples. For trap sampling, pathway P1 was used equally by both LMIC and HIC studies. Pathway P3 was preferred in HICs, whereas LMICs applied it to both trap and composite samples. Pathway P4 was predominantly associated with composite sampling across studies. Fig. 5a displays the co-occurrence map of study domains assigned to all 94 unique studies included in the systematic review. Domains were identified by examining titles, keywords, and abstracts; where structured abstracts were unavailable, the first page was reviewed. The details of the identified study domains are provided in Supplementary Table T11. Each study could be mapped to one or more domains. A total of eight domains were identified: Domain A - Outbreak detection and investigation (n = 33): studies using WES to detect, investigate, or retrospectively link outbreaks. Domain B - Disease prevalence (n = 79): studies using WES to supplement clinical or sentinel surveillance for estimating disease burden. Domain C - AMR prevalence (n = 73): studies assessing the spread of AMR organisms or genes in the community. Domain D - Mechanisms of AMR (n = 31): studies exploring the physiological, molecular, or genetic mechanisms of transmission of resistance via wastewater. Domain E - Wastewater usage monitoring (n = 19): studies evaluating microbial diversity in reclaimed or contaminated water used for agriculture or irrigation. Domain F - Environmental health (n = 62): studies investigating links between wastewater microbial diversity and anthropogenic or environmental factors. Domain G - One Health (n = 23): studies addressing human-animal-environment interactions, including cross-species transmission and intersectoral AMR evidence. Domain H - Method validation (n = 20): studies focused on validating WES methods for sample collection or testing, including assessment of assay sensitivity and standardization of protocols. The co-occurrence matrix of the identified domains was mapped onto a network to understand relatedness between the study domains (Fig. 5b). The network analysis revealed that domains B (Disease Prevalence), C (AMR Prevalence), and F (Environmental Health) are closely grouped. These are considered core domains that guide key questions in the field, such as how WES can detect the presence or absence of Salmonella sp., assess AMR, and understand the role of environmental factors in pathogen establishment. Domains D (Mechanisms of AMR) and E (Wastewater Usage Monitoring) act as bridge or support domains, connecting strongly with the core domains and providing important context, such as understanding resistance mechanisms or identifying sources of contamination through wastewater reuse. Domains G (One Health) and H (Method Validation) are linked to multiple domains but in smaller numbers, indicating their cross-cutting nature. These domains contribute to broader perspectives, such as intersectoral collaboration or methodological rigor. Finally, domain A (Outbreak Detection and Investigation) is relatively isolated, with fewer connections to other domains. This suggests that studies in this domain often require specialized approaches and may not overlap extensively with broader surveillance objectives. To evaluate the completeness of reporting on the methods, the quality assessment template (Supplementary Table T5) was used to determine whether the studies thoroughly documented the wastewater methodology. The extracted data is provided in the supplementary dataset 2. Supplementary Fig. S6 illustrates how studies (n = 94) reported the methodology across various assessment criteria. Nearly all studies clearly provided information on sampling site details (n = 79), sample processing (n = 86), and testing methods (n = 79). Additionally, the choice of site, based on the hypothesis or central question posed by the study (n = 60), sample collection details (n = 60), details of testing procedures, including reagents (n = 53), and sample transport conditions with transient times (n = 46), were reported inconsistently. The reporting of quality control procedures used for laboratory methods remains the only criterion that is not frequently reported (n = 13). Therefore, although studies document aspects such as site selection, sample handling, transport, and testing with reasonable consistency, they often overlook the quality control measures needed to ensure reproducibility of results. In view of the lack of standardized methodology or guidance on Salmonella wastewater surveillance, we have attempted to outline recommendations that could support countries in initiating WES for Salmonella (Fig. 6). Fig. 6 provides detailed technical guidance on various steps that should be considered while implementing WES for Salmonella . It is also essential for the reproducibility of the selected method that all information for the chosen steps is included, either within the manuscript, supplementary materials, or as an online-published protocol during publication. To assist in framing the right questions, the knowledge gained in this systematic review is synthesized into a decision tool (Table 2). The developed decision tool offers essential context for defining the public health goals of establishing WES for Salmonella , including its scope and domain. The core domains help shape the main questions, while the supporting domains add additional objectives to strengthen the impact of the central questions. Cross-cutting domains provide context when multi-sectoral engagement is necessary. Niche domains can be used in conjunction with other domains to frame questions but may often require specific objectives that may not be relevant to different sectors. The framed question then guides the selection of suitable output data and sites to meet the goals, as well as justifying the testing pathway based on the available resources and infrastructure. The chosen testing pathway can be made reproducible by following the recommendations in Fig. 6. Therefore, using the decision tool may help define an objective with the testing methodology and promote stakeholder involvement in the developing interdisciplinary field of WES. Table 2 : Decision tool for developing a reason-based WES program in resource-limited settings Public Health Scope Domains (Type) Output Type (Site selection) Testing Pathways Monitoring for the detection of importations Monitoring of community-level baselines Monitoring of changes in risk factors Monitoring of epidemiological changes Monitoring the effect of changes in healthcare practices Optimization of resource and budget allocation Evidence generation for developing and implementing public health policies Evidence generation for evaluation of public health policies Evidence generation for calibration of public health interventions Outbreak detection or identification (Niche) Disease Prevalence (Core) AMR Prevalence (Core) and/or Mechanisms (Supporting) Monitoring wastewater usage (Supporting) Environmental Health (Core) One Health (Cross-cutting) Analytical Method Validation (Cross-cutting) Cross-sectional (single site, single time) Time-series (single site, multiple times) Longitudinal (multiple sites, multiple times) Spatial (multiple sites) Spatio-temporal (multiple sites, multiple times) Hierarchical (nested structure of sites, e.g., small to large drains) Network (interconnected site, e.g, multiple drains linked to WWTP) P1 P2 P3 P4 P5 P6 Discussion This systematic review identified substantial heterogeneity in laboratory methodologies used for WES of Salmonella sp. across 94 studies spanning 36 countries. A total of 102 distinct methodological approaches were documented, reflecting wide variation in sampling strategies, sample types, and laboratory testing protocols. Grab sampling was the most common method, although trap and composite sampling were also used, often without consistent reporting on sample handling or transport conditions. Culture-based methods were frequently employed, yet many studies relied solely on molecular or genomic techniques, underscoring the lack of standardized testing pathways. Six distinct methodological pathways were identified, each reflecting different combinations of protocol steps and resource contexts. Domain mapping showed that studies often addressed multiple public health objectives, with disease prevalence, AMR, and environmental health emerging as core domains. Outbreak detection and method validation were underrepresented, suggesting a need for targeted investment in these areas. The interdisciplinary nature of Salmonella WES, encompassing One Health, environmental monitoring, and epidemiology, reflects its evolving role in integrated public health action (Rosofsky and Vorhees 2023; Milazzo et al. 2025). The identified domains emphasize the importance of Salmonella WES in providing community-level signals for disease prevalence, information on pathogen importation, characterization of AMR emergence and spread, and assessment of environmental transmission risk, which directly align with the WHO-defined potential use cases for routine WES (World Health Organization 2024b). The findings must be interpreted in the context of infrastructural and epidemiological realities in LMICs, particularly in the WHO South-East Asia Region. Rapid urbanization has outpaced the development of sanitation infrastructure, resulting in fragmented wastewater systems that complicate the recovery and surveillance of pathogens (Hyun et al. 2019; Sinharoy et al. 2019). Wastewater reuse for agriculture and urban needs has increased exposure to waterborne diseases, including typhoid fever (Furumai 2008; Kumar and Goyal 2020; Tortajada 2020; Qiu et al. 2021; Ramm and Sielska 2023; Qi et al. 2024; Heyde et al. 2025). Historically, S. Typhi was among the first pathogens monitored in sewage to identify asymptomatic carriers (Moore 1971), and this approach has been used to locate transmission hotspots (Andrews et al. 2020). Despite its early promise, WES for typhoid has remained underutilized, with poliovirus being the only pathogen for which environmental surveillance is widely institutionalized (World Health Organization 2003; GPEI 2015; GPEI 2023; Singh et al. . The infrastructure established for wastewater sample collection and molecular testing for polio ES could be leveraged to initiate Salmonella WES. The COVID-19 pandemic catalyzed renewed interest in wastewater-based surveillance, demonstrating its utility for early detection and public health decision-making (Singh et al. 2024; Pang et al. 2025). The post-pandemic surge in publications reflects this shift, with studies emerging from both LMICs and HICs. Unconventional sampling sources; such as aircraft, refugee ships, and border entry points, have expanded the scope of surveillance (Li et al. 2023; Jones et al. 2024; Morfino et al. 2025; St-Onge et al. 2025). However, the dominance of grab sampling, limited use of Moore swabs (Sikorski and Levine 2020), and inconsistent reporting of sample handling suggest that feasibility often outweighs methodological rigor. The lack of standardization has direct implications for reproducibility and comparability. Our quality assessment revealed that while most studies reported basic methodological components, critical details such as quality control procedures, reagent specifications, and validation criteria were frequently omitted (Westgard and Westgard 2016; Boulbes et al. 2018; Fowotade et al. 2018; Andrews et al. 2020; Badrick 2021). This gap limits the utility of published protocols for replication or scale-up in other settings. To address this, we categorized the methods into six distinct pathways based on protocol steps and resource contexts. This classification offers a pragmatic framework for selecting appropriate methodologies aligned with laboratory capacity and surveillance goals. Importantly, the decision tool developed from this synthesis (Table 2) enables stakeholders to align methodological choices with public health objectives, whether for outbreak detection, disease burden estimation, or AMR monitoring. The interdisciplinary nature of Salmonella WES calls for integrated policy frameworks. Surveillance programs should be embedded within broader public health strategies that facilitate cross-sectoral collaboration among human, animal, and environmental health agencies (Rosofsky and Vorhees 2023; Milazzo et al. 2025). This aligns with the Quadripartite One Health Joint Plan of Action and supports the development of multisectoral early warning systems for emerging pathogens. (One Health High-Level Expert Panel et al. 2022) Despite efforts to optimize the search strategy, the review may have missed relevant studies published in non-indexed journals or in languages other than English. The reliance on published literature means that methodological details were often incomplete or inconsistently reported, particularly regarding quality control procedures, reagent specifications, and validation criteria. This limited the ability to fully assess reproducibility and operational feasibility. The review also did not include grey literature, internal reports, or unpublished protocols, which may contain valuable insights into real-world implementation challenges. While the decision tool and pathway classification are grounded in extracted data, they have not yet been validated through field testing or stakeholder consultation, which we aim to do as a next step. The review focused exclusively on wastewater or contaminated surface water sources and excluded other environmental matrices such as surface water or sludge, which may also be relevant for Salmonella surveillance in certain contexts. To advance WES for typhoid control, we recommend the development and adoption of standardized protocols that are both scientifically robust and operationally feasible across varied infrastructure settings. These protocols should include clear specifications for sample collection (e.g., volume, timing, and type), transport conditions, and laboratory testing workflows along with validation criteria for result interpretation. The consistent use of Moore swabs, which have demonstrated superior sensitivity in flowing wastewater environments, should be encouraged in typhoid-endemic regions (Sikorski and Levine 2020). Furthermore, quality control procedures must be embedded throughout the surveillance process, with explicit documentation of reagents, test conditions, and validation criteria to ensure methodological transparency and reliability (Westgard and Westgard 2016; Boulbes et al. 2018; Fowotade et al. 2018; Andrews et al. 2020; Badrick 2021). Surveillance systems must be tailored to local resource contexts. The six methodological pathways identified in this review provide a flexible framework for laboratories with differing capacities. These pathways can be matched to surveillance objectives using the decision tool developed herein, which links public health goals to appropriate testing strategies and site selection models. Such alignment is particularly critical in LMICs, where infrastructure constraints necessitate pragmatic and cost-effective approaches. Strategically, Salmonella WES should be integrated into national disease control programs and linked to typhoid conjugate vaccine (TCV) deployment. Environmental data can complement clinical surveillance and inform the timing and targeting of vaccination campaigns, especially in settings where blood culture-based diagnostics are limited (World Health Organization 2018; World Health Organization 2019; World Health Organization 2024b; Kumar et al. 2025). Future studies should evaluate the sensitivity and cost-effectiveness of composite sampling approaches, particularly in decentralized and low-flow wastewater systems common in LMICs. Comparative assessments of the six identified methodological pathways under field conditions are needed to determine which combinations of protocol steps yield the most reliable results across diverse environmental contexts. Additionally, research should explore the integration of WES data with clinical and AMR surveillance systems to enhance early warning capabilities and inform vaccine deployment strategies. The operational feasibility of implementing surveillance in unconventional settings; such as border crossings, refugee camps, and transportation hubs, also requires further study, especially in light of emerging global health threats. Finally, interdisciplinary implementation models that align with One Health frameworks should be piloted and evaluated. These models must address not only technical performance but also governance, stakeholder engagement, and sustainability in resource-limited settings. Conclusion WES for Salmonella sp., particularly S . Typhi, presents a promising yet underutilized tool for public health action in resource-limited settings. Standardized protocol and its harmonized implementation are thus a key success factor for Salmonella WES, however, it necessitates scientific and operational research to achieve the outcome. Leveraging existing systems for polio ES has potential to expedite the implementation of Salmonella WES. This review offers a foundation for methodological harmonization, strategic integration, and interdisciplinary collaboration. By aligning surveillance design with public health objectives and local capacities, stakeholders can advance robust, scalable systems that support typhoid control and broader One Health goals. Declarations Author contributions. LS and KH conceptualized the study. LS, KH, VS, TMM, and YJ conducted the abstract screening. The full-text review was led by VS, with all authors providing input for consensus building. All authors contributed to data extraction and manuscript preparation. Disclaimer : The work represents the personal opinion of the authors and not that of the organization for whom they work. Competing interests. Authors declare no competing interests. Funding. This work was supported by the European Commission’s Health Emergency Preparedness and Response Authority (HERA) under the project “Support strategies, capacity and data for global wastewater and environmental surveillance” (CP-CA-24-94.1/2), Contribution Agreement No. HERA/2024/SI2.921807. Additional funding was provided through internal resources of the World Health Organization (WHO). The funders had no role in the design, execution, analysis, or interpretation of the review. Acknowledgements. The authors thank Dr Tapasyapreeti Mukhopadhyay, Consultant at WHO-SEARO, for her contributions towards putting the manuscript together. 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Environ Sci Technol 52:4869-4877 doi: 10.1021/acs.est.8b00163 Yanagimoto K, Yamagami T, Uematsu K, Haramoto E (2020) Characterization of Salmonella Isolates from Wastewater Treatment Plant Influents to Estimate Unreported Cases and Infection Sources of Salmonellosis. Pathogens 9 doi: 10.3390/pathogens9010052 Zhang CM et al. (2019) Characterization and evolution of antibiotic resistance of Salmonella in municipal wastewater treatment plants. Journal of Environmental Management 251:8 doi: 10.1016/j.jenvman.2019.109547 Table Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation20251029.docx Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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\u003cem\u003eSalmonella\u003c/em\u003esp.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7998434/v1/2e3159eb4ad46cfdf6b123ab.png"},{"id":94992746,"identity":"671b6159-5a7a-4640-8131-9c42f9c863a6","added_by":"auto","created_at":"2025-11-03 07:22:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":278962,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Geographical distribution of countries reporting environmental surveillance of \u003cem\u003eSalmonella\u003c/em\u003e sp. (n=94); \u003cstrong\u003eb\u003c/strong\u003e Number of studies from different countries over the years 1980 to 2020 in 10-year intervals, and after 2020, along with the socioeconomic status. LIC: low-income countries, LMIC: low- and middle-income countries, UMIC: upper middle-income countries, and HIC: high-income countries.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7998434/v1/48f2ce33e2245ff1c2deb959.png"},{"id":94992739,"identity":"be28e283-14dc-48b8-9aa4-d778756459ee","added_by":"auto","created_at":"2025-11-03 07:22:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":283350,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Count of steps used by each method (n=102); \u003cstrong\u003eb\u003c/strong\u003e Upset plot for culture steps used in the methods, showing intersection size, that is, the count of methods using culture steps either alone or in combination.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7998434/v1/31576a993d2205d115ce058c.png"},{"id":94992680,"identity":"900e2ad2-e39b-41bc-a373-632d71b961b0","added_by":"auto","created_at":"2025-11-03 07:21:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":267248,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003e\u0026nbsp;Clustering of methods used to identify the pathways involved in wastewater sample processing and testing, based on the findings from 79 extracted studies that included 87 different methods. For each pathway cluster, the number of methods is indicated; \u003cstrong\u003eb\u003c/strong\u003eFraction plot showing the use of pathways for testing by sample type and country category (LMIC group includes LIC, LMIC, and UMIC).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7998434/v1/b52736e057f3de0c350fccfb.png"},{"id":94992745,"identity":"9c785f45-93d9-4281-8954-b6ed06ae8079","added_by":"auto","created_at":"2025-11-03 07:22:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":261871,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Study domain co-occurrence map (n=94); \u003cstrong\u003eb\u003c/strong\u003e Network of co-occurrence map of study domains(n=94).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7998434/v1/6fd9d8d029e85fa5a9b546b8.png"},{"id":94992675,"identity":"f4f3020b-94c8-4eeb-ba95-aba16f66c1e0","added_by":"auto","created_at":"2025-11-03 07:21:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":394375,"visible":true,"origin":"","legend":"\u003cp\u003eRecommendations for details to be included while reporting the WES methodologies for \u003cem\u003eSalmonella\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7998434/v1/d20654d21c54b509ac802b73.png"},{"id":95312022,"identity":"62bd4b85-22f7-4f94-a80a-fb6764f9afa2","added_by":"auto","created_at":"2025-11-06 15:42:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2433989,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7998434/v1/e5501bc7-f8bc-4ce9-99e6-75330136e194.pdf"},{"id":94992682,"identity":"7b2f1fb8-ca55-4833-b86c-a5b39dc873f5","added_by":"auto","created_at":"2025-11-03 07:22:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":549709,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation20251029.docx","url":"https://assets-eu.researchsquare.com/files/rs-7998434/v1/e8aa28c89339345df187b4dc.docx"},{"id":94992669,"identity":"0e15fe25-6cdf-4351-bd62-ac1d34fac0e6","added_by":"auto","created_at":"2025-11-03 07:21:57","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":200467,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7998434/v1/a63b1b4bb75a26953911ffbf.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Systematic review on the laboratory methodology for conducting wastewater and environmental surveillance (WES) for Salmonella","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWastewater and environmental surveillance (WES) is well-established for poliovirus surveillance as a core part of the Global Polio Eradication Initiative (World Health Organization \u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; GPEI \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; GPEI \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; GPEI \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and gained further prominence during the COVID-19 pandemic as a valuable tool for understanding the disease burden in communities (Levy et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) The success of WES in COVID-19 has sparked interest in its use to monitor other pathogens of public health concern (Grassly et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Prioritization of pathogens for WES remains a fundamental question for optimizing resource utilization and ensuring operational flexibility and adaptability in the event of outbreaks caused by new pathogens (Tiwari et al. \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Toro et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; World Health Organization \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). Proposed prioritization frameworks emphasize several key factors for successful WES adaptation, including the public health significance of the pathogen, the usefulness of WES data for public health actions, and the analytical feasibility of conducting WES (Toro et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; World Health Organization \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). Prioritizing a pathogen within this framework helps align WES efforts with broader public health goals.\u003c/p\u003e\u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e infections, particularly with typhoid and paratyphoid serovars, continue to pose a substantial burden on global public health systems, and remain a significant challenge in low- and middle-income countries (LMICs), including within the World Health Organization's South-East Asia Region (SEAR) (Kim et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Non-typhoid and non- paratyphoid \u003cem\u003eSalmonella\u003c/em\u003e serovars. infect both humans and various animals, making them a significant concern for zoonotic transmission and for both animal husbandry and the food industry (Hoelzer et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ferrari et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Asymptomatic carriers and subclinical infections play a key role in maintaining the transmission chain of \u003cem\u003eSalmonella\u003c/em\u003e infections (Khanam et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, accurately determining the true prevalence of \u003cem\u003eSalmonella\u003c/em\u003e-related diseases requires supplementing clinical surveillance with serosurveys or contact tracing during outbreaks (Cao et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Uwanibe et al. \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, current methods for additional surveillance have limited sensitivity. For instance, the clinical diagnosis of typhoid and paratyphoid often relies on non-specific Widal tests or blood cultures, both of which have low sensitivity due to suboptimal sampling times post-incubation period (Andualem et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mawazo et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Despite the World Health Organization not recommending the Widal test, it remains widely used in clinical practice in our region. In addition to its limited sensitivity, the Widal test is prone to cross-reactivity with other pathogens, further reducing its diagnostic specificity. Additionally, estimating HlyE IgG antibodies using ELISA is the preferred method for serosurveys on typhoid and paratyphoid prevalence; however, HlyE antibodies can exhibit cross-reactivity, as many other bacteria also express HlyE (Kumar et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Aiemjoy et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a result, there is a need to establish supplementary tools to accurately estimate the true prevalence of the infections caused by \u003cem\u003eSalmonella\u003c/em\u003e Typhi and Paratyphi.\u003c/p\u003e\u003cp\u003eSince \u003cem\u003eSalmonella\u003c/em\u003e is present in wastewater due to shedding in the feces of both symptomatic and asymptomatic individuals, depending on the stage of infection, WES has proven effective in evaluating its community burden in endemic countries, complementing existing clinical surveillance and serosurveys (Yanagimoto et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Uzzell et al. \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Abraham et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Besides assessing community-level disease burdens, WES can also generate data on circulating strains and antimicrobial resistance\u0026mdash;provided the bacteria can be cultured\u0026mdash;which directly informs vaccination and antimicrobial resistance (AMR) strategies, offering significant public health benefits for \u003cem\u003eSalmonella\u003c/em\u003e monitoring (Yan et al. \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Diemert and Yan \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, the public health importance of \u003cem\u003eSalmonella\u003c/em\u003e and the utility of WES make it a priority pathogen for WES implementation (World Health Organization \u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe primary challenge of \u003cem\u003eSalmonella\u003c/em\u003e WES lies in analytical feasibility, due to the heterogeneous and variable factors outside the laboratory that affect sample collection and quality. Unlike high-income countries with centralized and closed sewage systems, most \u003cem\u003eSalmonella\u003c/em\u003e Typhi and Paratyphi-endemic countries, particularly in the SEAR region (Abraham et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Jahan et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Oktaria et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), frequently rely on decentralized, informal, or mixed drainage networks, including open drains, septic tanks, and combined sewer-stormwater systems, which are often poorly maintained and vulnerable to contamination during monsoons (Sotelo et al. \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nasim et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, the pre-examination factors such as variability in infrastructure, flow dynamics, and ambient conditions complicate sample collection and pathogen recovery and demands context-specific adaptations to sampling and testing protocols. Laboratory capacity constraints, including cold chain logistics and molecular diagnostics standardization and result interpretations, further limit the applicability of WES protocols (Jahan et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Oktaria et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Owusu et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInherent variability in sampling site characteristics, public health goals, and laboratory capacity underscores the urgent need to develop harmonized methodologies that are scientifically sound, operationally practical, and adaptable to diverse infrastructure contexts, particularly in low- and middle-income countries. This systematic review was conducted to evaluate the scientific and operational feasibility of laboratory methods for detecting \u003cem\u003eSalmonella\u003c/em\u003e in wastewater, aiming to guide the development of harmonized, context-specific field and laboratory protocols that can support regional public health goals like integrated disease surveillance and inform the deployment of typhoid conjugate vaccine (TCV) in endemic areas and LMIC. The review assessed the completeness of methodological reporting; such as site selection, sample handling, and quality control to identify critical gaps in methodological reporting that might impede reproducibility and scalability. The identified methodologies were further categorized as pathways to match protocol steps with the wastewater sampling and socio-economic status of reporting countries. Finally, the review also identified the primary public health domains that researchers utilize to develop a framework for aligning surveillance objectives with laboratory capacities and infrastructure realities.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy design.\u003c/b\u003e This qualitative systematic review was designed and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (Page et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A detailed protocol outlining the objectives, eligibility criteria, and methodological approach was developed before the initiation of the review and registered with the International Prospective Register of Systematic Reviews (PROSPERO) on August 5, 2024 (registration ID: CRD42024573052).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSearch Strategy.\u003c/b\u003e A comprehensive literature search was conducted to identify peer-reviewed studies describing laboratory methodologies for the detection or isolation of Typhoid \u003cem\u003eSalmonella\u003c/em\u003e serovar. The initial search was conducted on September 10, 2024, using three major scientific databases: PubMed, EMBASE, and Web of Science. It was updated on May 31, 2025, to include recent publications. The specific combination of databases was chosen because it achieves a 90\u0026ndash;95% recall rate in over 80% of reported systematic reviews (Bramer et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The search strategy was designed to retrieve studies relevant to \u003cem\u003eSalmonella\u003c/em\u003e Typhi surveillance in environmental matrices, with a focus on wastewater and surface water. Briefly, a na\u0026iuml;ve search was conducted on PubMed after identifying relevant MESH and MAJR terms for WES of \u003cem\u003eSalmonella\u003c/em\u003e Typhi. The query used was - \"\u003cem\u003eSalmonella\u003c/em\u003e\"[Mesh] AND (\"Wastewater-Based Epidemiological Monitoring\" [Mesh] OR \"Environmental Monitoring\"[Mesh] OR \"Sewage/microbiology\"[MAJR] OR \"Wastewater/microbiology\"[MAJR]). The easyPubMed package in R was used to import the na\u0026iuml;ve search results, which were analyzed with the litsearchr package in R to identify keywords in an unbiased manner (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Supplementary Table T3) (Fantini \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Grames et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The identified keywords were used to create PubMed search queries using Boolean operators and wildcard symbols (e.g., *). The search query was optimized to evaluate its sensitivity against a benchmark set of 16 studies on WES for typhoid (Supplementary Table T4). The search terms were refined until all 16 benchmark studies were captured (Supplementary Fig. S2 and Supplementary Table T5). Once 100% sensitivity was achieved for the benchmark studies, the final PubMed query was translated into EMBASE and Web of Science formats using the polyglot application (Kung \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The final search queries for each database are provided in (Supplementary Table T6).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSystematic review process.\u003c/b\u003e The final search was conducted in May 2025, and results were imported into the Covidence platform, which automatically removed duplicates. Remaining duplicates were manually reviewed and excluded. Title and abstract screening was then performed, with inclusion and exclusion criteria detailed in Supplementary Table T7. Two authors independently reviewed the studies, resolving conflicts by consensus among all authors. Full texts were retrieved for eligible studies and screened again against the criteria. The Covidence platform was also used to prepare templates for data extraction and methodological quality assessment, as described in Supplementary Tables T8 and T9. Data extraction was performed independently by two authors, with disagreements resolved by consensus. Supplementary Table T1 presents the PRISMA checklist.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData analysis and visualization.\u003c/b\u003e A narrative synthesis was conducted due to the variability in study designs, sampling strategies, and laboratory methods. Data visualization involved subgroup analysis based on the methodological areas (e.g., sampling, processing, testing), and patterns were identified across the studies. Additionally, the findings were integrated into a decision tool to help frame public health objectives and methodological choices.\u003c/p\u003e\u003cp\u003ePython (version 3.12.7) was used within the Spyder IDE (version 6.0.7) to perform data analysis and visualization, using a collection of specialized libraries. Pandas handled data import and transformation; NumPy supported numerical calculations; and SciPy was used for estimating Jaccard distances and performing chi-squared tests. The silhouette score was calculated with Scikit-learn, while NetworkX enabled network analysis. GeoPandas managed geospatial polygon data for countries, Matplotlib produced standard plots, Seaborn generated heatmaps, and UpSetPlot was used to create UpSet diagrams.\u003c/p\u003e\u003cp\u003eGeographical mapping of studies was conducted using cultural raster map shapefiles obtained from Natural Earth and analyzed with the GeoPandas library in Python. Since some studies reported using multiple methods or samples, the methods were identified to aid analysis. To find commonalities among the methods, pathway analysis was performed. During this process, methods were clustered using the Jaccard distance approach based on factors such as the economic status of countries, sample types, and the presence or absence of specific protocol steps. For simplicity, the LMIC classification included all countries from the LIC (low-income countries), LMIC, and UMIC (upper middle-income countries) categories. The samples analyzed included grab samples, trap samples, and composite samples. Protocol steps included processing, culture, biotyping (using biochemicals and other biotyping techniques), serotyping (via the Kauffman-White scheme and PCR), antimicrobial susceptibility testing, genotyping (using molecular assays and ARGs), and genomics methods, including targeted sequencing, whole-genome sequencing, and metagenomics. The methods were grouped using the Jaccard distance metric with average linkage, and the optimal number of clusters was determined through the silhouette score and the elbow method. A Chi-square test of independence was performed using SciPy to evaluate whether different protocol steps occur disproportionately across various pathways. A p-value less than 0.05 was considered statistically significant, indicating that a protocol step influences the clustering. To identify under- or over-representation of a step within a specific pathway, residual analysis was performed, with values greater than 2 indicating a significant association and values less than \u0026minus;\u0026thinsp;2 indicating no significant association.\u003c/p\u003e\u003cp\u003eStudy domains were identified based on the titles, keywords, and abstracts of the selected studies. Eight domains were recognized: A) outbreak detection/investigation, B) disease prevalence, C) AMR prevalence, D) mechanisms of AMR, E) wastewater monitoring, F) environmental health, G) One Health, and H) method validation. Some studies encompassed more than one domain. The co-occurrence of domains was calculated using NumPy. To visualize the core domains and their connections to other domains, the co-occurrence matrix was interpreted as an undirected weighted graph with NetworkX. The network comprises nodes representing individual domains, with node size proportional to the number of studies in each domain. Edges indicate co-occurrences between domains, with edge width reflecting the strength of these co-occurrences. Additionally, a force-directed spring layout was used to position strongly related domains closer together.\u003c/p\u003e\u003cp\u003e\u003cb\u003eReporting bias.\u003c/b\u003e The review aimed to eliminate bias during both the searching and review stages. To minimize search bias, multiple databases were utilized, and the search string was refined through unbiased keyword selection and by evaluating the search strategy against a set of benchmark studies. For study selection bias, two authors independently reviewed the abstracts during the screening process to determine eligibility for full-text review, and two authors independently conducted the data extraction.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 2,007 articles were identified across PubMed, Embase, and Web of Science. After removing 686 duplicates, 1,321 records were screened by title and abstract, resulting in 1,143 exclusions. Of the 178 full-text articles assessed, 94 met the eligibility criteria and were included in the review. Studies were excluded for reasons such as incomplete methodology, non-peer-reviewed status, or publication in a language other than English. Included studies were further classified by methodological quality assessment into five categories: excellent (n = 20), robust (n = 22), good (n = 22), fair (n = 22), and low (n = 8). The PRISMA workflow for the systematic review is presented in Fig. 1. The extracted data and quality assessment data are provided as supplementary datasets 1 and 2, respectively. As some studies used multiple samples and multiple methods, the extracted data were further refined to obtain the methods used for each sample in different studies. This resulted in the identification of 102 methods. Table 1 summarizes the sample types and methodology used by the included studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe selected studies represented a wide geographic distribution across 36 countries, five of them in SEAR countries (Fig. 2a). Most (n = 89) were single-country studies, while five involved multiple countries. According to the World Bank\u0026apos;s income classification, the studies spanned two LICs, ten LMICs, eight UMICs, and 16 high-income countries (HICs). The United States of America and India contributed the highest number of studies published after 2020 (Fig. 2b).\u003c/p\u003e\n\u003cp\u003eThe included studies employed a range of sampling methods to collect wastewater, each reflecting different operational contexts and surveillance goals (Table 1). Most studies reported using grab sampling (n = 62), which involves collecting a predefined volume of wastewater in a sterile container at a single point in time. This was followed by trap sampling (n = 10), a passive technique in which a receptacle, often a Moore swab, is exposed to flowing wastewater over a set period to capture microorganisms. Composite sampling (n = 9) was also employed, involving autosamplers that collect wastewater at regular intervals over an extended period. Additionally, some studies (n = 7) employed a combination of grab and trap sampling, while five studies did not specify the type of sample collected. One study uniquely used sewage sludge as the sample type.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSample handling after collection was reported in 61 studies (Table 1). Among these, 56 studies described the transportation of samples under cold chain conditions to preserve microbial integrity. Additionally, 52 studies provided details on the time elapsed between sample collection and laboratory processing. Of these, 49 studies processed samples within 24 hours, while three studies reported delays exceeding 24 hours.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSample processing, defined as the steps taken before initiating the microbiological procedure, was also mentioned by various methods (n=74) (Table 1). The methods reported processing using filtration (n=32) to trap microorganisms or remove large debris, centrifugation (n=8) to pellet the microorganisms, dilution or serial dilution (n=5) to reduce inhibitory components, processing of the Moore swab to extract its contents (n=3), using specially designed magnetic particles that bind with bacteria (n=1), or a combination of methods (n=23) to remove debris and inhibitors and trap microorganisms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe laboratory testing could be further resolved in different protocol steps, including bacterial culture, bacterial enumeration, phenotypic characterization of observable bacterial traits (biochemical identification, serotyping, antimicrobial susceptibility, and other methods), genotypic characterization based on bacterial nucleic acid (PCR and other methods), and sequencing (Fig. 3a). Two studies used a novel bacteriophage-based method that employed detecting \u003cem\u003eSalmonella\u003c/em\u003e-specific bacteriophages as an indicator for \u003cem\u003eSalmonella\u003c/em\u003e. It was noted that the use of different protocol steps is influenced by the central question or hypothesis that these studies aimed to address.\u003c/p\u003e\n\u003cp\u003eBacterial cultivation was attempted by 70% of the methods (n=71), as shown in Fig. 3a. This step typically involved enrichment in non-selective or selective media, followed by selective culture or isolation of \u003cem\u003eSalmonella\u003c/em\u003e sp., using standard culture media. However, not all studies included a culture step as part of their methodology, some relied solely on molecular or alternative detection techniques. Fig. 3b illustrates the combination of culture steps used in different methods (n = 67), excluding four methods that reported using standard methods (ISO 6579, ISO 19250, FDA Bioanalytical manual protocol for \u003cem\u003eSalmonella\u003c/em\u003e, and APHA standard method) and one method that exclusively used bacteriophage-specific methods. Among those that performed culture, the most common protocol involved enrichment, selective enrichment, and selective culture (n = 32), followed by selective enrichment and culture (n = 7). Additionally, four methods reported bacterial enumeration using serial dilutions and the most probable number (MPN) method to estimate bacterial load.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFig. 3a also illustrates how different phenotypic characterization assays were primarily used to characterize isolated bacteria based on observable traits, such as growth in specific media, serotype, or antimicrobial susceptibility. The phenotypic methods included biotyping with biochemical media (n = 48), serotyping (n = 26), antimicrobial susceptibility testing (n = 47), and other phenotypic techniques mainly involving phage typing (n \u0026nbsp;= 3), Matrix-Assisted Laser Desorption/Ionization Time-of-Flight, MALDI-TOF (n = 2), and both MALDI-TOF and phage typing ( n=1). Biotyping utilized standard biochemical identification techniques, either manual (n = 32), automated (n = 12), or a combination of both (n = 3). One study did not specify the biochemicals used. For serotyping, most studies employed the Kauffman-White serotyping scheme to characterize \u003cem\u003eSalmonella\u003c/em\u003e isolates (n = 24). Two studies reported the use of a PCR-based serotyping scheme. Regarding antimicrobial susceptibility, the majority of studies used the disc diffusion assay (n = 36), followed by broth microdilution (n = 4), automated systems (n = 3), a combination of disc diffusion and automated systems (n = 2), and a combination of disc diffusion and broth microdilution (n = 1). One of the studies also utilized resistance transfer testing to understand the mechanism of AMR gene transfer to a susceptible host.\u003c/p\u003e\n\u003cp\u003eGenotypic characterization of bacterial nucleic acid by molecular assays primarily included variants of PCR (n = 65), as well as other molecular assays, such as pulsed field gel electrophoresis (n = 10), plasmid analysis (n = 2), DNA fingerprinting (n = 1), fluorescence in situ hybridization (n = 1), and sequencing (n = 28). The methods reported included qPCR (n=22), PCR (n=18), molecular assays for antimicrobial resistance genes (ARG) (n=12), and a combination of qPCR and ARG PCR (n=2). Other than that, one study each reported using multiplex PCR, RT-PCR BioFire FilmArray\u0026reg; panel, crystal digital PCR, high-throughput qPCR, PCR with virulence marker PCR, culture PCR with high-throughput qPCR, 16S RNA PCR with PCR, qPCR with high-throughput qPCR, PCR with qPCR, and qPCR with denaturing gradient gel electrophoresis. Most used genomic method was whole genome sequencing, WGS (n=11), followed by untargeted or shotgun metagenomics (n=6), and 16S rDNA targeted sequencing (n=2). One study each reported targeted sequencing, 16S rRNA sequencing, ARG genes sequencing, untargeted metagenomics, long and short read sequencing using Nanopore and Illumina sequencing, 16S rRNA sequencing with WGS, 16S RNA sequencing with metagenomics, and 16S rDNA sequencing with biomarker sequencing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further understand the methodology of WES for \u003cem\u003eSalmonella\u003c/em\u003e sp., the identified methods were further characterized in terms of common pathways from sample collection to testing. In this approach, 87 different methods from 79 studies were employed (Supplementary Table T10). Fifteen methods from 14 studies were excluded from this analysis because no sample was specified, standard procedures were not discussed in detail, bacteriophage surveillance was not included, or methods used for stored isolates were not specified.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the clustering, a total of six pathways were identified (Fig. 4a and Supplementary Fig. S3). The details of each method mapped to a pathway are provided in Supplementary Table T10. Eight methods shared a pathway P1, mainly involving a culture step followed by identification using molecular methods, with a slight association with a processing step. Pathway P2 was the most used pathway, with 43 methods. P2 has a strong association with the processing step, culture, biotyping of isolates, and antimicrobial susceptibility testing. The pathway was mildly associated with molecular assays, with low association to other steps. P3 was shared by seven methods and was associated with culture, biotyping, serotyping, and AST. Seven studies shared pathway P4, which was strongly associated with a processing step and the use of molecular assays for characterization. P4 also had a moderate association with culture and genomics methods. Pathway P5 was the second most used pathway, with 15 methods, primarily involving molecular characterization after a processing step. Lastly, pathway P6 was shared by seven methods and included a processing step and testing using a genomics method. Among the six identified methodological pathways, statistical analysis revealed that all protocol steps, except genotyping for ARG were significantly associated with specific pathways (p \u0026lt; 0.01, Supplementary Fig. S4). This indicates that most steps showed distinct patterns of inclusion across the pathways. Post-hoc residual analysis further confirmed that all steps, except genotyping for ARG, exhibited differential usage across pathways, supporting the robustness of the clustering approach (Supplementary Fig. S5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFig. 4b illustrates the distribution of methodological pathways across sample types in LMIC and HIC settings. Both LMICs and HICs studies commonly employed pathways P2, P5, and P6 for grab samples. Pathway P2 was used at similar rates in both income groups, while P5 was more prevalent in LMICs and P6 in HICs settings. In LMICs, P5 was also applied to composite and trap samples. For trap sampling, pathway P1 was used equally by both LMIC and HIC studies. Pathway P3 was preferred in HICs, whereas LMICs applied it to both trap and composite samples. Pathway P4 was predominantly associated with composite sampling across studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFig. 5a displays the co-occurrence map of study domains assigned to all 94 unique studies included in the systematic review. Domains were identified by examining titles, keywords, and abstracts; where structured abstracts were unavailable, the first page was reviewed. The details of the identified study domains are provided in Supplementary Table T11. Each study could be mapped to one or more domains. A total of eight domains were identified: Domain A - Outbreak detection and investigation (n = 33): studies using WES to detect, investigate, or retrospectively link outbreaks. Domain B - Disease prevalence (n = 79): studies using WES to supplement clinical or sentinel surveillance for estimating disease burden. Domain C - AMR prevalence (n = 73): studies assessing the spread of AMR organisms or genes in the community. \u0026nbsp;Domain D - Mechanisms of AMR (n = 31): studies exploring the physiological, molecular, or genetic mechanisms of transmission of resistance via wastewater. Domain E - Wastewater usage monitoring (n = 19): studies evaluating microbial diversity in reclaimed or contaminated water used for agriculture or irrigation. Domain F - Environmental health (n = 62): studies investigating links between wastewater microbial diversity and anthropogenic or environmental factors. Domain G - One Health (n = 23): studies addressing human-animal-environment interactions, including cross-species transmission and intersectoral AMR evidence. Domain H - Method validation (n = 20): studies focused on validating WES methods for sample collection or testing, including assessment of assay sensitivity and standardization of protocols.\u003c/p\u003e\n\u003cp\u003eThe co-occurrence matrix of the identified domains was mapped onto a network to understand relatedness between the study domains (Fig. 5b). The network analysis revealed that domains B (Disease Prevalence), C (AMR Prevalence), and F (Environmental Health) are closely grouped. These are considered core domains that guide key questions in the field, such as how WES can detect the presence or absence of \u003cem\u003eSalmonella\u003c/em\u003e sp., assess \u0026nbsp;AMR, and understand the role of environmental factors in pathogen establishment. Domains D (Mechanisms of AMR) and E (Wastewater Usage Monitoring) act as bridge or support domains, connecting strongly with the core domains and providing important context, such as understanding resistance mechanisms or identifying sources of contamination through wastewater reuse. Domains G (One Health) and H (Method Validation) are linked to multiple domains but in smaller numbers, indicating their cross-cutting nature. These domains contribute to broader perspectives, such as intersectoral collaboration or methodological rigor. Finally, domain A (Outbreak Detection and Investigation) is relatively isolated, with fewer connections to other domains. This suggests that studies in this domain often require specialized approaches and may not overlap extensively with broader surveillance objectives.\u003c/p\u003e\n\u003cp\u003eTo evaluate the completeness of reporting on the methods, the quality assessment template (Supplementary Table T5) was used to determine whether the studies thoroughly documented the wastewater methodology. The extracted data is provided in the supplementary dataset 2. Supplementary Fig. S6 illustrates how studies (n = 94) reported the methodology across various assessment criteria. Nearly all studies clearly provided information on sampling site details (n = 79), sample processing (n = 86), and testing methods (n = 79). Additionally, the choice of site, based on the hypothesis or central question posed by the study (n = 60), sample collection details (n = 60), details of testing procedures, including reagents (n = 53), and sample transport conditions with transient times (n = 46), were reported inconsistently. The reporting of quality control procedures used for laboratory methods remains the only criterion that is not frequently reported (n = 13). Therefore, although studies document aspects such as site selection, sample handling, transport, and testing with reasonable consistency, they often overlook the quality control measures needed to ensure reproducibility of results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn view of the lack of standardized methodology or guidance on \u003cem\u003eSalmonella\u003c/em\u003e wastewater surveillance, we have attempted to outline recommendations that could support countries in initiating WES for \u003cem\u003eSalmonella\u003c/em\u003e (Fig. 6). Fig. 6 provides detailed technical guidance on various steps that should be considered while implementing WES for \u003cem\u003eSalmonella\u003c/em\u003e. It is also essential for the reproducibility of the selected method that all information for the chosen steps is included, either within the manuscript, supplementary materials, or as an online-published protocol during publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo assist in framing the right questions, the knowledge gained in this systematic review is synthesized into a decision tool (Table 2). The developed decision tool offers essential context for defining the public health goals of establishing WES for \u003cem\u003eSalmonella\u003c/em\u003e, including its scope and domain. The core domains help shape the main questions, while the supporting domains add additional objectives to strengthen the impact of the central questions. Cross-cutting domains provide context when multi-sectoral engagement is necessary. Niche domains can be used in conjunction with other domains to frame questions but may often require specific objectives that may not be relevant to different sectors. The framed question then guides the selection of suitable output data and sites to meet the goals, as well as justifying the testing pathway based on the available resources and infrastructure. The chosen testing pathway can be made reproducible by following the recommendations in Fig. 6. Therefore, using the decision tool may help define an objective with the testing methodology and promote stakeholder involvement in the developing interdisciplinary field of WES.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Decision tool for developing a reason-based WES program in resource-limited settings\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"592\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePublic Health Scope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDomains\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Type)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutput Type\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Site selection)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTesting Pathways\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eMonitoring for the detection of importations\u003c/li\u003e\n \u003cli\u003eMonitoring of community-level baselines\u003c/li\u003e\n \u003cli\u003eMonitoring of changes in risk factors\u003c/li\u003e\n \u003cli\u003eMonitoring of epidemiological changes\u003c/li\u003e\n \u003cli\u003eMonitoring the effect of changes in healthcare practices\u003c/li\u003e\n \u003cli\u003eOptimization of resource and budget allocation\u003c/li\u003e\n \u003cli\u003eEvidence generation for developing and implementing public health policies\u003c/li\u003e\n \u003cli\u003eEvidence generation for evaluation of public health policies\u003c/li\u003e\n \u003cli\u003eEvidence generation for calibration of public health interventions\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eOutbreak detection or identification (Niche)\u003c/li\u003e\n \u003cli\u003eDisease Prevalence (Core)\u003c/li\u003e\n \u003cli\u003eAMR Prevalence (Core) and/or Mechanisms (Supporting)\u003c/li\u003e\n \u003cli\u003eMonitoring wastewater usage (Supporting)\u003c/li\u003e\n \u003cli\u003eEnvironmental Health (Core)\u003c/li\u003e\n \u003cli\u003eOne Health (Cross-cutting)\u003c/li\u003e\n \u003cli\u003eAnalytical Method Validation (Cross-cutting)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eCross-sectional (single site, single time)\u003c/li\u003e\n \u003cli\u003eTime-series (single site, multiple times)\u003c/li\u003e\n \u003cli\u003eLongitudinal (multiple sites, multiple times)\u003c/li\u003e\n \u003cli\u003eSpatial (multiple sites)\u003c/li\u003e\n \u003cli\u003eSpatio-temporal (multiple sites, multiple times)\u003c/li\u003e\n \u003cli\u003eHierarchical (nested structure of sites, e.g., small to large drains)\u003c/li\u003e\n \u003cli\u003eNetwork (interconnected site, e.g, multiple drains linked to WWTP)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eP1\u003c/li\u003e\n \u003cli\u003eP2\u003c/li\u003e\n \u003cli\u003eP3\u003c/li\u003e\n \u003cli\u003eP4\u003c/li\u003e\n \u003cli\u003eP5\u003c/li\u003e\n \u003cli\u003eP6\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis systematic review identified substantial heterogeneity in laboratory methodologies used for WES of \u003cem\u003eSalmonella\u003c/em\u003e sp. across 94 studies spanning 36 countries. A total of 102 distinct methodological approaches were documented, reflecting wide variation in sampling strategies, sample types, and laboratory testing protocols. Grab sampling was the most common method, although trap and composite sampling were also used, often without consistent reporting on sample handling or transport conditions. Culture-based methods were frequently employed, yet many studies relied solely on molecular or genomic techniques, underscoring the lack of standardized testing pathways. Six distinct methodological pathways were identified, each reflecting different combinations of protocol steps and resource contexts.\u003c/p\u003e\n\u003cp\u003eDomain mapping showed that studies often addressed multiple public health objectives, with disease prevalence, AMR, and environmental health emerging as core domains. Outbreak detection and method validation were underrepresented, suggesting a need for targeted investment in these areas. The interdisciplinary nature of \u003cem\u003eSalmonella\u003c/em\u003e WES, encompassing One Health, environmental monitoring, and epidemiology, reflects its evolving role in integrated public health action (Rosofsky and Vorhees 2023; Milazzo et al. 2025). The identified domains emphasize the importance of Salmonella WES in providing community-level signals for disease prevalence, information on pathogen importation, characterization of AMR emergence and spread, and assessment of environmental transmission risk, which directly align with the WHO-defined potential use cases for routine WES (World Health Organization 2024b).\u003c/p\u003e\n\u003cp\u003eThe findings must be interpreted in the context of infrastructural and epidemiological realities in LMICs, particularly in the WHO South-East Asia Region. Rapid urbanization has outpaced the development of sanitation infrastructure, resulting in fragmented wastewater systems that complicate the recovery and surveillance of pathogens (Hyun et al. 2019; Sinharoy et al. 2019). Wastewater reuse for agriculture and urban needs has increased exposure to waterborne diseases, including typhoid fever (Furumai 2008; Kumar and Goyal 2020; Tortajada 2020; Qiu et al. 2021; Ramm and Sielska 2023; Qi et al. 2024; Heyde et al. 2025).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHistorically,\u003cem\u003e\u0026nbsp;S.\u003c/em\u003e Typhi was among the first pathogens monitored in sewage to identify asymptomatic carriers (Moore 1971), and this approach has been used to locate transmission hotspots (Andrews et al. 2020). Despite its early promise, WES for typhoid has remained underutilized, with poliovirus being the only pathogen for which environmental surveillance is widely institutionalized (World Health Organization 2003; GPEI 2015; GPEI 2023; Singh et al.\u0026nbsp;. The infrastructure established for wastewater sample collection and molecular testing for polio ES could be leveraged to initiate Salmonella WES. The COVID-19 pandemic catalyzed renewed interest in wastewater-based surveillance, demonstrating its utility for early detection and public health decision-making (Singh et al. 2024; Pang et al. 2025).\u003c/p\u003e\n\u003cp\u003eThe post-pandemic surge in publications reflects this shift, with studies emerging from both LMICs and HICs. Unconventional sampling sources; such as aircraft, refugee ships, and border entry points, have expanded the scope of surveillance (Li et al. 2023; Jones et al. 2024; Morfino et al. 2025; St-Onge et al. 2025). However, the dominance of grab sampling, limited use of Moore swabs (Sikorski and Levine 2020), and inconsistent reporting of sample handling suggest that feasibility often outweighs methodological rigor.\u003c/p\u003e\n\u003cp\u003eThe lack of standardization has direct implications for reproducibility and comparability. Our quality assessment revealed that while most studies reported basic methodological components, critical details such as quality control procedures, reagent specifications, and validation criteria were frequently omitted (Westgard and Westgard 2016; Boulbes et al. 2018; Fowotade et al. 2018; Andrews et al. 2020; Badrick 2021). This gap limits the utility of published protocols for replication or scale-up in other settings.\u003c/p\u003e\n\u003cp\u003eTo address this, we categorized the methods into six distinct pathways based on protocol steps and resource contexts. This classification offers a pragmatic framework for selecting appropriate methodologies aligned with laboratory capacity and surveillance goals. Importantly, the decision tool developed from this synthesis (Table 2) enables stakeholders to align methodological choices with public health objectives, whether for outbreak detection, disease burden estimation, or AMR monitoring.\u003c/p\u003e\n\u003cp\u003eThe interdisciplinary nature of \u003cem\u003eSalmonella\u003c/em\u003e WES calls for integrated policy frameworks. Surveillance programs should be embedded within broader public health strategies that facilitate cross-sectoral collaboration among human, animal, and environmental health agencies (Rosofsky and Vorhees 2023; Milazzo et al. 2025). This aligns with the Quadripartite One Health Joint Plan of Action and supports the development of multisectoral early warning systems for emerging pathogens. (One Health High-Level Expert Panel et al. 2022)\u003c/p\u003e\n\u003cp\u003eDespite efforts to optimize the search strategy, the review may have missed relevant studies published in non-indexed journals or in languages other than English. The reliance on published literature means that methodological details were often incomplete or inconsistently reported, particularly regarding quality control procedures, reagent specifications, and validation criteria. This limited the ability to fully assess reproducibility and operational feasibility. The review also did not include grey literature, internal reports, or unpublished protocols, which may contain valuable insights into real-world implementation challenges. While the decision tool and pathway classification are grounded in extracted data, they have not yet been validated through field testing or stakeholder consultation, which we aim to do as a next step. The review focused exclusively on wastewater or contaminated surface water sources and excluded other environmental matrices such as surface water or sludge, which may also be relevant for \u003cem\u003eSalmonella\u003c/em\u003e surveillance in certain contexts.\u003c/p\u003e\n\u003cp\u003eTo advance WES for typhoid control, we recommend the development and adoption of standardized protocols that are both scientifically robust and operationally feasible across varied infrastructure settings. These protocols should include clear specifications for sample collection (e.g., volume, timing, and type), transport conditions, and laboratory testing workflows along with validation criteria for result interpretation. The consistent use of Moore swabs, which have demonstrated superior sensitivity in flowing wastewater environments, should be encouraged in typhoid-endemic regions (Sikorski and Levine 2020). Furthermore, quality control procedures must be embedded throughout the surveillance process, with explicit documentation of reagents, test conditions, and validation criteria to ensure methodological transparency and reliability (Westgard and Westgard 2016; Boulbes et al. 2018; Fowotade et al. 2018; Andrews et al. 2020; Badrick 2021).\u003c/p\u003e\n\u003cp\u003eSurveillance systems must be tailored to local resource contexts. The six methodological pathways identified in this review provide a flexible framework for laboratories with differing capacities. These pathways can be matched to surveillance objectives using the decision tool developed herein, which links public health goals to appropriate testing strategies and site selection models. Such alignment is particularly critical in LMICs, where infrastructure constraints necessitate pragmatic and cost-effective approaches.\u003c/p\u003e\n\u003cp\u003eStrategically, \u003cem\u003eSalmonella\u003c/em\u003e WES should be integrated into national disease control programs and linked to typhoid conjugate vaccine (TCV) deployment. Environmental data can complement clinical surveillance and inform the timing and targeting of vaccination campaigns, especially in settings where blood culture-based diagnostics are limited (World Health Organization 2018; World Health Organization 2019; World Health Organization 2024b; Kumar et al. 2025).\u003c/p\u003e\n\u003cp\u003eFuture studies should evaluate the sensitivity and cost-effectiveness of composite sampling approaches, particularly in decentralized and low-flow wastewater systems common in LMICs. Comparative assessments of the six identified methodological pathways under field conditions are needed to determine which combinations of protocol steps yield the most reliable results across diverse environmental contexts.\u003c/p\u003e\n\u003cp\u003eAdditionally, research should explore the integration of WES data with clinical and AMR surveillance systems to enhance early warning capabilities and inform vaccine deployment strategies. The operational feasibility of implementing surveillance in unconventional settings; such as border crossings, refugee camps, and transportation hubs, also requires further study, especially in light of emerging global health threats.\u003c/p\u003e\n\u003cp\u003eFinally, interdisciplinary implementation models that align with One Health frameworks should be piloted and evaluated. These models must address not only technical performance but also governance, stakeholder engagement, and sustainability in resource-limited settings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWES for \u003cem\u003eSalmonella\u003c/em\u003e sp., particularly \u003cem\u003eS\u003c/em\u003e. Typhi, presents a promising yet underutilized tool for public health action in resource-limited settings. Standardized protocol and its harmonized implementation are thus a key success factor for \u003cem\u003eSalmonella\u003c/em\u003e WES, however, it necessitates scientific and operational research to achieve the outcome. Leveraging existing systems for polio ES has potential to expedite the implementation of \u003cem\u003eSalmonella\u003c/em\u003e WES. This review offers a foundation for methodological harmonization, strategic integration, and interdisciplinary collaboration. By aligning surveillance design with public health objectives and local capacities, stakeholders can advance robust, scalable systems that support typhoid control and broader One Health goals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions.\u0026nbsp;\u003c/strong\u003eLS and KH conceptualized the study. LS, KH, VS, TMM, and YJ conducted the abstract screening. The full-text review was led by VS, with all authors providing input for consensus building. All authors contributed to data extraction and manuscript preparation. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer\u003c/strong\u003e: The work represents the personal opinion of the authors and not that of the organization for whom they work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests.\u0026nbsp;\u003c/strong\u003eAuthors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u0026nbsp;\u003c/strong\u003eThis work was supported by the European Commission\u0026rsquo;s Health Emergency Preparedness and Response Authority (HERA) under the project \u0026ldquo;Support strategies, capacity and data for global wastewater and environmental surveillance\u0026rdquo; (CP-CA-24-94.1/2), Contribution Agreement No. HERA/2024/SI2.921807. Additional funding was provided through internal resources of the World Health Organization (WHO). The funders had no role in the design, execution, analysis, or interpretation of the review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u0026nbsp;\u003c/strong\u003eThe authors thank Dr Tapasyapreeti Mukhopadhyay, Consultant at WHO-SEARO, for her contributions towards putting the manuscript together. The authors also thank Ms. Akanksha Panwar and Ms. Faustina Gomez for providing administrative support during the review process. Authors would also like to thank Dr Hussain Rasheed, Dr Vinod Bura and Sam. Ilham for supporting this work administratively during initial phase. The authors would also like to acknowledge support from the SEARO Library, especially Ms. Mohita Dawar and Ms. Charu Relan, for arranging the full texts of the articles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability.\u0026nbsp;\u003c/strong\u003eData is provided within the manuscript or supplementary information files available online.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbraham D et al. (2025) Wastewater surveillance for Salmonella Typhi and its association with seroincidence of enteric fever in Vellore, India. PLOS NEGLECTED TROPICAL DISEASES 19:e0012373 doi: 10.1371/journal.pntd.0012373\u003c/li\u003e\n\u003cli\u003eAcheamfour CL et al. (2021) Levels of Salmonella enterica and Listeria monocytogenes in Alternative Irrigation Water Vary Based on Water Source on the Eastern Shore of Maryland. 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Journal of Environmental Management 251:8 doi: 10.1016/j.jenvman.2019.109547\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Salmonella Typhi, wastewater surveillance, laboratory methodology, South-East Asia","lastPublishedDoi":"10.21203/rs.3.rs-7998434/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7998434/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e infections continue to pose a significant public health challenge in low- and middle-income countries (LMICs), particularly in Southeast Asia. Wastewater and environmental surveillance (WES) offers a promising approach for supplementing clinical and field surveillance methods for early detection and monitoring. This systematic review aimed to evaluate laboratory methodologies for detecting \u003cem\u003eSalmonella spp.\u003c/em\u003e in wastewater and contaminated surface waters. Following the PRISMA 2020 guidelines, PubMed, EMBASE, and Web of Science (1980–2024) were searched for studies that described sampling and laboratory methods for detecting \u003cem\u003eSalmonella\u003c/em\u003e in environmental water. Data extraction and quality assessment used standardized templates. Out of 2,007 records, 94 studies met the inclusion criteria. Methodological heterogeneity was high, with grab sampling and Moore swabs predominating; \u003cem\u003eSalmonella \u003c/em\u003edetection methods included culture, PCR, and genomic sequencing. Fewer than 30% of studies reported comprehensive quality control. Based on the systematic review, a need for standardized, context-adapted protocol was identified to enhance WES utility for \u003cem\u003eSalmonella\u003c/em\u003e surveillance in LMICs.\u003c/p\u003e","manuscriptTitle":"Systematic review on the laboratory methodology for conducting wastewater and environmental surveillance (WES) for Salmonella","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 07:14:08","doi":"10.21203/rs.3.rs-7998434/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"edce716f-4c9b-484f-a0e0-65263da39a43","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T07:20:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-03 07:14:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7998434","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7998434","identity":"rs-7998434","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}