Submitted:
01 June 2026
Posted:
02 June 2026
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Abstract
Wastewater-based surveillance (WBS) has emerged as a promising population-level monitoring tool for a wide range of pathogens and serves as a cost-effective complement to clinical surveillance. However, the evidence base supporting current WBS practices has been generated almost exclusively in high-income countries (HICs) with established laboratory infrastructure, reliable cold-chain logistics, functional sewerage systems, and mature public health reporting frameworks. Directly transferring these approaches to low- and middle-income countries (LMICs) where WBS may be most needed—introduces substantial challenges across the entire surveillance chain, from sample collection and laboratory analysis to data interpretation and public health communication. Sample collection in LMICs often differs fundamentally from that in HICs. In many settings, samples need to be collected from fecally contaminated rivers, open sewage channels, drainage systems, pit latrines, or other non-sewered sanitation systems rather than centralized sewerage system. This heterogeneity complicates both sampling strategies and the interpretation of surveillance results. Next, laboratory analysis also faces significant constraints. Many LMICs lack reliable supply chains for laboratory consumables and reagents. Limited market demand often results in small import volumes, short remaining shelf lives upon arrival, and higher costs. Furthermore, the limited number of suppliers reduces competition, making essential consumables disproportionately expensive. Many of the analytical products and technologies developed in high-income countries are already costly in their countries of origin and become even less accessible when imported into resource-constrained settings. Data interpretation presents another major challenge. The diversity of sampling locations and sanitation systems makes direct comparisons between sites difficult. In addition, the limited number of published WBS studies from LMICs restrict opportunities for contextual benchmarking and validation. The lack of sufficient case studies across diverse environmental, epidemiological, and infrastructural settings further hampers the development of robust interpretation frameworks. Institutional capacity limitations also affect both data interpretation and communication. Many LMICs have insufficient public health and environmental health laboratory networks to support routine WBS activities. Moreover, the absence of established institutions and governance mechanisms for integrating WBS data into public health decision-making limits the utility of surveillance outputs. Weak coordination among governmental agencies, public health authorities, environmental regulators, and research institutions further constrains the effective use of generated information. Importantly, pathogen priorities in LMICs often differ substantially from those in high-income settings. While respiratory viruses have dominated much of the recent WBS literature, foodborne and waterborne pathogens remain major public health concerns in many LMICs. Similarly, antimicrobial resistance (AMR)-related pathogens such as Mycobacterium tuberculosis and Salmonella enterica serovar Typhi represent critical surveillance targets due to their high disease burden and public health significance. This perspective critically examines each stage of the WBS surveillance chain, identifies where current approaches are poorly suited to LMIC contexts, and proposes a reoriented framework for WBS implementation grounded in local realities rather than adapted from high-income country models. We argue that WBS in LMICs will achieve its full public health potential only if it is supported by locally generated evidence, context-specific case studies, and methodologies designed explicitly for the unique operational, infrastructural, and epidemiological challenges of these settings. WBS in LMICs should therefore be treated as a distinct methodological and operational field rather than as a simplified extension of existing practices developed in high-income countries.
Keywords:
wastewater-based surveillance
; low- and middle-income countries
; pathogen surveillance
; antimicrobial resistance
; non-sewered sanitation systems
; global health
Introduction
The COVID-19 pandemic demonstrated that wastewater-based surveillance (WBS) can provide timely, cost-effective, and less biased community-level signals that complement clinically reported surveillance and case-based reporting[1,2]. This experience generated unprecedented global momentum for the adoption of WBS as a routine public health tool. Since then, applications have expanded to encompass a broad range of targets, including SARS-CoV-2, influenza viruses, poliovirus, mpox virus, antimicrobial resistance (AMR) genes, and enteric pathogens[3,4]. Notably, wastewater monitoring already popular at the COVID-19 era, with studies identifying 25 pathogen families—primarily Picornaviridae, including polio and non-polio enteroviruses[5]—as well as, chemical markers, and illicit drugs[6,7]. Across these diverse applications, the fundamental advantage of WBS remains the same: it captures population-level signals independently of healthcare-seeking behaviour, symptom status, diagnostic testing availability, and reporting practices[8,9]. These characteristics make WBS particularly valuable in settings where conventional clinical surveillance is limited or incomplete[4,10].
Global distribution of WBS capacity represents many inequities that the approach has the potential to address. The vast majority of WBS studies have been conducted in North America, Europe, Australia, and high-income regions of Asia[5,11]. In contrast, sub-Saharan Africa, South Asia, and much of Latin America—regions that bear disproportionate burdens of infectious diseases, AMR, and environmental contamination—remain severely underrepresented in the evidence base[4,12,13,14]. This is not merely an issue of research equity; it has direct operational consequences. Without locally generated data, public health agencies in low- and middle-income countries (LMICs) cannot establish context-specific detection thresholds, evaluate assay performance under local environmental and wastewater conditions, or develop the institutional capacity required to sustain WBS as a routine public health function. Long-term implementation requires not only technical validation but also the establishment of sustainable monitoring programmes, trained personnel, reliable supply chains, standardized surveillance frameworks, and capacity for data interpretation and public health decision-making[4].
This perspective needs to examines the entire WBS value chain—from sampling site selection and pathogen prioritization to data analysis, interpretation, and translation into public health action—from an explicitly LMIC perspective. At each stage, we identify where assumptions embedded in current WBS practice reflect high-income country conditions, where these assumptions may fail in LMIC contexts, and what adaptations are necessary to make WBS a practical, sustainable, and impactful surveillance tool in the settings where its potential public health value may be greatest.
Sample collection: the first and most consequential challenge
One of the most fundamental challenges for WBS in LMICs is the lack of centralized sewerage infrastructure. Unlike HICs, where wastewater is typically collected through extensive and well-defined sewer networks, many LMICs rely heavily on decentralized and informal sanitation systems[12,14]. Consequently, identifying representative sampling locations remains one of the greatest operational challenges for WBS implementation. In HICs settings, WBS is predominantly based on 24-hour composite influent samples collected at WWTPs[1,11,15]. These systems benefit from relatively predictable hydraulic and solids retention times, well-characterized catchment populations, and stable wastewater flows. As a result, WWTP influent provides a population-representative composite sample that can support quantitative epidemiological inference. In many HICs, WWTPs serve 70–100% of the population within their catchments, making wastewater measurements broadly representative of community-level pathogen circulation.
These assumptions do not hold in most LMIC settings. Globally, an estimated 2.4 billion people lack access to safely managed sanitation services, and sewer network coverage in many urban areas of sub-Saharan Africa and South Asia remains below 30–50%, with some cities exhibiting substantially lower coverage[16]. Much of the population relies on on-site sanitation systems, including pit latrines, septic tanks, shared toilets, and informal drainage networks[17]. Consequently, a large proportion of human waste never reaches centralized treatment facilities. For example, in cities such as Kampala, Dhaka, Kinshasa, Kathmandu and Nairobi, most wastewater is managed through decentralized sanitation systems or discharged into open drains, surface waters, and informal channels rather than being conveyed to WWTPs. A WBS programme centred exclusively on WWTP influent therefore captures only a subset of the urban population—typically residents with access to formal sewerage infrastructure, who are often wealthier and healthier than those living in underserved communities. Ironically, populations are at greatest risk of infectious diseases, AMR, and inadequate healthcare access may be systematically excluded from surveillance.
This limitation affects both study design and data interpretation. Detection of a pathogen at the sampling site serving a small fraction of the population cannot be translated into city-wide prevalence estimates with the same confidence as in highly sewered systems. Conversely, the absence of pathogen detection cannot be interpreted as evidence of absence within the broader community, as the populations most affected by transmission may not be connected to the sampled network. Furthermore, poorly characterized wastewater flows, variable hydraulic retention times, and intermittent discharge patterns introduce additional uncertainty into the interpretation of pathogen concentrations and temporal trends. Effective WBS in LMICs therefore requires a shift from infrastructure-centred to population-centred surveillance design. Rather than relying exclusively on centralized treatment facilities, sampling strategies should be tailored to local sanitation realities and focus on locations that best represent the populations of interest.
Several alternative approaches have demonstrated feasibility in LMIC contexts. Open-drain and surface-water sampling can capture wastewater originating from non-sewered communities where household waste is discharged into informal drainage channels. Studies from Bangkok, Kathmandu, and other cities in Southeast Asia have successfully detected SARS-CoV-2, mpox virus, and other pathogens in open drains and receiving waters, often generating signals that preceded or coincided with clinical case reporting. However, these matrices are subject to substantial dilution, rainfall effects, and complex inhibitory substances that can complicate molecular detection and quantification.
Sentinel community surveillance offers another promising approach. Sampling can be concentrated in strategically selected locations such as schools, markets, healthcare facilities, refugee camps, transportation hubs, and informal settlements. Compared with municipal WWTP sampling, sentinel sites can provide higher spatial resolution and more representative coverage of high-risk populations. This approach has been widely used for poliovirus surveillance and is readily adaptable for monitoring other pathogens of public health importance.
Healthcare facility wastewater represents an additional high-value surveillance target. Hospital wastewater contains concentrated pathogen signals from both diagnosed and undiagnosed patients and has been successfully used for surveillance of SARS-CoV-2, mpox virus, and AMR pathogens. In settings with limited community surveillance infrastructure, healthcare facility wastewater can provide an efficient early-warning system for emerging pathogens and resistance threats.
Finally, pit latrine and septic tank sampling may offer one of the most direct means of accessing populations that are entirely excluded from sewer-based surveillance. Representative sampling of on-site sanitation systems has already been demonstrated for poliovirus and helminth surveillance and is increasingly being explored for broader pathogen monitoring. In communities where on-site sanitation predominates, latrine and septic tank sampling may provide population-level information that conventional WWTP-based approaches miss entirely.
Taken together, these considerations highlight that successful WBS implementation in LMICs cannot rely on simply transferring surveillance models developed in highly sewered high-income countries. Instead, surveillance systems must be designed around local sanitation realities, prioritizing population representativeness over infrastructure convenience. Such an approach is essential if WBS is to realize its full potential as an equitable public health surveillance tool in settings where the need for timely population-level data is often greatest.
Practical Constraints on Sample Collection
Beyond site selection, the practical logistics of sample collection present significant challenges specific to LMIC contexts. Maintaining a reliable cold chain — essential for preserving nucleic acid integrity between collection and analysis — is often difficult across much of sub-Saharan Africa and South Asia, where power outages, limited refrigeration capacity, and long transport times to central laboratories can lead to sample degradation prior to processing. Studies have shown that SARS-CoV-2 RNA and other nucleic acids can degrade rapidly at ambient temperatures above 20–25°C, with substantial signal loss occurring within hours under typical tropical field conditions — which represent the norm rather than the exception in many LMIC settings.
Mitigation strategies include on-site stabilization using RNA preservation buffers such as RNAlater or comparable formulations, immediate concentration and preservation of nucleic acids at the point of collection and streamlined protocols that minimize the time between sampling and stabilization. Mobile laboratory platforms — including vehicle-mounted or rapidly deployable molecular units — offer an emerging solution by bringing extraction and initial processing capabilities directly to the field, thereby substantially reducing dependence on cold-chain infrastructure. However, validation of these platforms under real-world LMIC field conditions remains an active and high-priority area of research.
Composite sampling — involving flow-weighted collection over 24-hour periods using automated refrigerated autosamplers — is standard practice in HICs WBS and provides time-integrated, quantitatively robust samples. In many LMIC settings, however, such automated systems are scarce, costly, dependent on stable power supply, and require technical expertise for operation and maintenance. As a result, grab sampling — single time-point manual collection — is more commonly used, although it introduces substantial temporal variability that can limit quantitative interpretation and comparability. There is therefore a need for systematic research to quantify the information loss when grab samples substitute for composite sampling in LMIC contexts, as well as to develop low-cost, semi-passive or non-automated concentration approaches that can partially mitigate these limitations.
Pathogen Selection: Whose Priorities Count?
The pathogens prioritized for WBS in HICs largely reflect the epidemiological profiles and public health concerns of those settings — including SARS-CoV-2, influenza, mpox, norovirus, and healthcare-associated AMR pathogens. These are entirely valid priorities in their respective contexts, but they do not necessarily align with the pathogens responsible for the greatest disease burden in LMICs, where diarrhoeal diseases, enteric fever, cholera, hepatitis A and E, arboviral infections, and helminth infections account for a disproportionate share of morbidity and mortality. As a result, a WBS system designed solely around HICs priorities risks generating surveillance outputs that are poorly aligned with local public health needs in LMIC settings.
At the same time, there is an important pragmatic advantage to initially focusing on pathogens already widely studied in high-income country WBS systems: for these organisms, baseline literature, methodological protocols, and contextual data — including shedding dynamics, decay kinetics, and proof-of-concept evidence — are already available, which facilitates methodological transfer and system development. In contrast, many pathogens of primary importance in LMICs have been far less studied in wastewater contexts, meaning that foundational parameters such as shedding rates, environmental persistence, and decay kinetics are often unknown or highly uncertain. This lack of basic data represents a major barrier to rapid expansion of WBS for locally relevant pathogens.
To address this mismatch, stakeholder-driven pathogen prioritization is essential. Engaging Ministries of Health, national public health institutes, clinicians, and community representatives in defining surveillance targets can help ensure that WBS programmes are aligned with locally relevant disease burdens rather than global research agendas. Several structured prioritization frameworks are available — including multi-criteria decision analysis and burden-of-disease-informed ranking tools — and these should be systematically incorporated into LMIC WBS programme design. Evidence from stakeholder engagement studies in sub-Saharan Africa, for example, consistently highlights cholera, typhoid, rotavirus, hepatitis viruses, poliovirus, and Klebsiella pneumoniae as high-priority targets, diverging substantially from the pathogen portfolios typically monitored in high-income country WBS systems.
Practical Constraints on Multiplex Surveillance
Even where priorities are appropriately defined, the capacity to monitor multiple pathogens simultaneously remains constrained by laboratory realities in LMIC settings. In high-income countries, WBS increasingly relies on multiplex qPCR panels, digital PCR, and metagenomic approaches to detect dozens of targets concurrently from a single sample. In contrast, many LMIC laboratories face persistent constraints, including unreliable reagent supply chains, cold chain requirements for enzyme-based assays that complicate storage and distribution, and limited availability of technical expertise for multiplex assay design, optimization, and quality assurance.
In this context, a tiered pathogen monitoring strategy is more operationally realistic and sustainable. This approach prioritizes a small number of locally validated, high-burden pathogens for routine surveillance, while reserving broader multiplex panels and more advanced molecular tools for outbreak investigation and response situations. Such a phased design is more feasible than attempting to replicate high-income country multiplex surveillance architectures from the outset.
Portable, field-deployable qPCR platforms — such as the Biomeme Franklin system, which operates without conventional cold-chain infrastructure and can be controlled via smartphone interfaces — offer a pragmatic option for targeted multiplex surveillance in resource-limited environments. However, technology deployment alone is insufficient. Sustainable implementation requires concurrent investment in capacity building, as platforms that perform well in demonstration or pilot studies may fail under routine operational conditions if local capacity for maintenance, troubleshooting, data interpretation, and quality control is not developed in parallel with hardware introduction.
Emerging Pathogens and Surveillance Readiness
LMICs majorly in SSA are disproportionately affected by emerging and re-emerging pathogens — including mpox clade Ib, Ebola virus, Marburg virus, and novel arboviruses — yet often have the least capacity to detect them rapidly through conventional clinical surveillance systems. WBS therefore offers a potentially transformative early warning function in these settings, with the ability to detect pathogen circulation at the community level before clinical systems become overwhelmed or before cases are formally recognized. Realizing this potential, however, requires investment in both targeted and broad-spectrum surveillance capacity. This includes the deployment of metagenomic approaches capable of identifying novel or unexpected pathogens, alongside more focused assays for known high-priority threats. Developing and sustaining this dual capacity in LMICs is a medium- to long-term objective, requiring coordinated investment in infrastructure, analytical platforms, and human capital to ensure that systems are both technically robust and operationally resilient.
Data Analysis: From Signal to Inference Under Resource Constraints
A key limitation in LMIC settings is the gap in computational and bioinformatic capacity. WBS data analysis — particularly for genomic workflows, variant tracking, and quantitative epidemiological modelling — requires substantial computational infrastructure and specialized expertise. In HICs research environments, high-performance computing clusters, cloud-based platforms, and dedicated bioinformatics software ecosystems are standard components of routine analytical pipelines. However, in many LMICs, reliable internet connectivity, sustained access to cloud computing services, local high-performance computing capacity, and trained bioinformaticians are often limited or inconsistently available. This creates a structural dependency: even when sample collection and molecular detection are feasible locally, data analysis is frequently outsourced to partners in high-income countries. Such dependencies can introduce delays, reduce local ownership of data, and undermine the long-term sustainability and autonomy of surveillance programmes.
Addressing this gap requires sustained investment in local bioinformatics capacity that goes beyond individual training. It necessitates the development of institutional infrastructure, including computational resources, data storage and management systems, and locally maintainable analytical pipelines. In parallel, open-source and lightweight bioinformatic tools designed for offline use and low-resource computing environments are more appropriate than cloud-dependent solutions in settings with unstable connectivity.
Platforms such as the EPI2ME Labs workflows from Oxford Nanopore Technologies, which can be run locally on standard laptop hardware, represent an important step toward more accessible bioinformatics in resource-limited contexts. However, further adaptation and optimization are still needed to ensure robustness, usability, and scalability under typical LMIC operating conditions.
Normalization, Quantification, and the Absence of Population Denominators and Clinical Data
Quantitative interpretation of WBS signals requires reliable population denominators, as estimates of how many people contribute to a sampled wastewater stream are essential for inferring community prevalence, tracking temporal trends, and enabling comparisons across sites. In high-income country systems, such denominators are typically derived from census data, sewer catchment mapping, and WWTP operational records. In LMIC settings, however — particularly for non-WWTP sampling points such as open drainage systems, sentinel community sites, or informal settlement outflows — population denominators are often unavailable, uncertain, or based on outdated or incomplete census information.
A further limitation in many LMIC contexts is the lack of reliable clinical case data to contextualize or triangulate WBS signals, which constrains the ability to validate trends or integrate wastewater surveillance into existing public health monitoring frameworks. This weakens the complementarity between environmental and clinical surveillance systems and limits quantitative interpretation.
Flow normalization — adjusting pathogen concentrations based on wastewater flow rates to account for dilution effects — is standard practice in high-income country WBS but depends on flow measurement infrastructure that is frequently absent in LMIC settings. As an alternative, surrogate normalization approaches using stable human fecal indicators such as pepper mild mottle virus (PMMoV), crAssphage, or human-associated Bacteroides markers provide a practical solution that does not require direct flow measurements and can partially reduce uncertainty arising from unknown population sizes.
However, the performance of these surrogate markers is likely to vary under LMIC-specific wastewater conditions, including high turbidity, highly variable fecal loading, intermittent discharge patterns, and diverse dietary and microbiome profiles. Systematic evaluation and local validation of normalization markers under such conditions therefore represent an important research priority for improving the quantitative robustness of WBS in resource-limited settings.
Analytical Sensitivity and the Silent False Negative Problem
LMIC wastewater matrices are frequently more challenging than those encountered in high-income country settings — higher chemical pollutants, turbidity, greater organic load, higher ambient temperatures, more variable pH, and greater microbial diversity all contribute to increased PCR inhibition and faster nucleic acid degradation. Methods validated in European or North American wastewater may perform significantly worse in tropical or subtropical matrices, producing false negatives not through genuine pathogen absence but through analytical failure. This silent false negative problem — where assays appear technically valid yet fail to detect genuine low-copy targets — is particularly acute in LMIC contexts and is rarely systematically assessed.
Before interpreting negative WBS results as evidence of pathogen absence in LMIC settings, it is essential to demonstrate that the analytical workflow can recover low-concentration spiked material from the local wastewater matrix under local laboratory conditions. Process positive controls — adding known quantities of target material or surrogate organisms to field samples before processing — should be standard practice in all LMIC WBS programmes, yet are inconsistently implemented. Building process control into standard operating procedures, training materials, and quality assurance frameworks for LMIC WBS is a practical and immediately actionable improvement that would substantially increase confidence in negative results.
WBS as a Public Health Tool: Complementary, Independent, or Both?
A critical and still underexplored question in WBS implementation is whether WBS data should function as a complement to clinical surveillance, as an independent surveillance system, or as both — and how this role may vary by context, pathogen, and underlying public health system capacity. Conflating these roles across settings has contributed to implementation of models that are poorly aligned with LMIC realities.
In high-income countries with relatively strong clinical surveillance systems, WBS is typically positioned as a complementary early warning tool. It can detect transmission trends before cases are reported through clinical systems, identify asymptomatic or unreported infections, and provide population-level signals that are not fully captured by symptom-based surveillance. In this framing, clinical case data serves as the reference standard against which WBS signals are interpreted and validated, which is appropriate in contexts where clinical surveillance is relatively timely, comprehensive, and reliable.
In many LMIC settings, however, clinical surveillance cannot be assumed to represent a reliable reference standard. Limited healthcare access constrained diagnostic capacity, and incomplete reporting systems often result in substantial underestimation of true disease burden. In such contexts, positioning WBS solely as a complementary tool to clinical surveillance risks underappreciating its value and mischaracterizing its role within the broader surveillance ecosystem.
Where clinical surveillance is weak or incomplete, WBS may instead function as a primary population-level signal — potentially representing the most consistent and scalable indicator of community disease dynamics. This reframing has important implications for interpretation, communication, and public health decision-making. A WBS signal that appears as early warning in a high-income country context may, in an LMIC setting, represent the most accurate available estimate of ongoing transmission rather than a precursor to clinical detection. In other words, what is considered “early warning” in one context may effectively constitute “primary evidence” of disease burden in another.
When WBS Should Function as a Complementary Tool
WBS functions most effectively as a complement to clinical surveillance in LMIC contexts where: clinical surveillance covers a defined population reliably, even if not comprehensively; the pathogen of interest has a significant asymptomatic carrier rate that clinical systems miss; and the public health system has the capacity to respond to early signals. Poliovirus surveillance is the clearest existing example — environmental surveillance of poliovirus has been successfully integrated with clinical acute flaccid paralysis surveillance in endemic and post-endemic settings for decades, providing community-level confirmation of transmission that clinical surveillance alone cannot deliver. The complementary model works here because both systems are functioning, covering overlapping populations, and feeding into a defined response framework.
Expanding this model to other enteric pathogens, respiratory viruses, and vector-borne diseases in LMICs requires investment in the clinical surveillance side of the equation, not just WBS. A WBS signal without a corresponding clinical system capable of case investigation, contact tracing, or targeted intervention has limited public health value identifies a problem that cannot be acted upon. The complementary model therefore demands parallel strengthening of both surveillance arms.
When WBS Can Function as an Independent Surveillance Signal
In settings where clinical surveillance for a specific pathogen is essentially absent — as is the case for most routine enteric pathogen and AMR surveillance in much of sub-Saharan Africa — WBS can provide independent evidence of community burden that has no clinical counterpart. In these contexts, WBS data should not be evaluated against clinical case counts (which are unreliable or absent) but against other contextual indicators: population health outcomes, treatment demand at health facilities, or trends in related pathogens. The DRC experience with mpox illustrates this clearly — WBS studies have detected high MPXV detection rates in settings with near-zero official case counts, suggesting that WBS is capturing real transmission that clinical systems cannot see rather than generating false positives.
Operating WBS as an independent signal requires higher analytical rigour — particularly for quality control, process positives, and confirmatory sequencing — precisely because there is no clinical reference standard against which to validate results. It also requires communication strategies that can present surveillance findings to decision-makers without the familiar anchor of clinical case data, which creates challenges for uptake and interpretation that are discussed further below.
Data Communication: Translating Signals into Action
The generation of a WBS signal is not the end of the surveillance process — it is the beginning of a communication and decision-making chain that must ultimately result in a public health action if the signal is to have value. In high-income countries, the translation pathway from WBS data to public health response has been extensively studied, with frameworks identifying key actors, decision points, communication formats, and institutional barriers. In LMICs, this pathway is largely uncharted, and the last mile — from data generation to public health action — is frequently the weakest link in the WBS chain.
Several specific challenges characterize data communication in LMIC WBS contexts. First, the institutional recipients of WBS data — Ministries of Health, national public health institutes, district health offices — may have limited familiarity with WBS methodology, making it difficult for them to interpret signals, assess their reliability, or determine appropriate responses. Second, data reporting formats developed for high-income country audiences — including trend visualizations, concentration metrics, and probabilistic risk estimates — may not be intuitive or actionable for decision-makers accustomed to case-based reporting. Third, the absence of established action thresholds means that even a technically proficient WBS system cannot tell decision-makers what level of signal requires a response — creating uncertainty that can paralyze action even when signals are clear.
Principles for Effective WBS Data Communication in LMICs
Effective WBS data communication in LMIC contexts should be guided by several principles drawn from health communication and implementation science.
Co-design with end users: Communication formats, reporting frequency, and action thresholds should be developed in direct collaboration with the public health officials, clinicians, and community leaders who will receive and act on WBS data. Approaches imposed by external researchers or high-income country partners are consistently less effective than those developed through genuine participatory design. Structured co-design processes — including joint working groups with Ministries of Health, scenario-based exercises using synthetic WBS signals to test decision-making, and iterative refinement of reporting formats — should be embedded in programme design from the outset, rather than added after data collection begins.
Actionable thresholds rather than raw concentrations: Raw measurements — such as gene copies per litre, Ct values, or detection frequencies — are not inherently actionable for decision-makers. What is needed are interpreted outputs: whether a signal exceeds a defined threshold for action, whether it is increasing or decreasing, and what it implies for likely disease burden in the catchment population. Developing locally calibrated action thresholds — even if initially based on international evidence and expert consensus in the absence of local datasets — is essential for ensuring that WBS outputs support decision-making rather than generate ambiguity.
Integration with existing reporting systems: WBS data are most effective when integrated into established health information systems — such as DHIS2 and other national health management information platforms widely used in LMICs — rather than being delivered through parallel reporting channels. Integration ensures that WBS signals reach the same decision-makers and analytical workflows that handle clinical, laboratory, and epidemiological data, enabling triangulation and reducing the cognitive burden associated with interpreting an additional data stream.
Transparent communication of uncertainty: WBS signals are associated with multiple layers of uncertainty, including sampling variability, matrix effects, PCR inhibition, and population denominator limitations, which are often more pronounced in LMIC contexts than in high-income settings. Communicating this uncertainty clearly, without undermining confidence in the system, requires careful balance. Overconfident reporting of uncertain signals can erode trust when results fluctuate, while excessive caution can inhibit timely action. Reporting frameworks that pair primary signals with simple, interpretable quality indicators — such as process control recovery rates, sampling coverage, and assay performance metrics — can support informed decision-making without requiring specialist expertise.
Managing social dimensions of targeted surveillance: In settings where WBS focuses on specific communities — such as informal settlements, markets, healthcare facilities, or refugee camps — the social implications of surveillance must be explicitly addressed. Surveillance perceived as externally imposed, disconnected from local priorities, or potentially stigmatizing can undermine trust, participation, and ultimately data quality. Community engagement — including clear communication with community leaders and affected populations about the purpose, methods, and intended use of data — is therefore not an ancillary ethical requirement but a core operational component of sustainable WBS in LMIC settings. Where surveillance targets populations defined by geographic or social vulnerability, ethical review processes should explicitly assess risks of stigmatization and incorporate proportionate safeguards into study design and data communication strategies.
Conclusions and Priority Recommendations
WBS holds genuine transformative potential for public health surveillance in LMICs, which is potentially greater than in high-income countries where clinical surveillance is already strong. Realizing this potential requires abandoning the assumption that LMIC implementation is simply a resource-constrained version of high-income country practice and instead treating the full WBS chain from sample collection to public health action as a distinct methodological and operational challenge in LMIC contexts.
Invest in non-sewered sampling strategies: Open drains, sentinel community sites, hospital wastewater, and pit latrine sampling must be systematically developed, validated, and standardized for LMIC contexts. WWTP-centred sampling alone cannot represent the populations that bear the highest disease burdens in most low-income urban settings.
Implement process controls as standard practice: Silent false negatives — analytical failures indistinguishable from true negatives — are a greater risk in LMIC matrices and undermine the credibility of negative results. Spike-and-recovery process controls should be mandatory in all LMIC WBS programmes.
Build local bioinformatic and analytical capacity: Sustainable WBS requires that data can be processed, analysed, and interpreted locally. Investment in computational infrastructure, workforce training, and lightweight open-source analytical tools is as important as investment in sequencing and molecular laboratory equipment.
Develop context-calibrated action thresholds: Regulators and public health officials in LMICs need translated, actionable outputs — not raw concentration data. Participatory threshold-setting processes, drawing on international evidence and local expert consensus, should be initiated in parallel with surveillance programme development.
Frame WBS role according to clinical surveillance capacity: The complementary versus independent role of WBS should be determined by the reliability of clinical surveillance in each context, not assumed to be identical across all settings. Where clinical surveillance is weak or absent, WBS should be positioned as a primary signal with appropriately enhanced quality assurance, rather than as a secondary complement to a non-existent reference standard.
WBS in LMICs is not a future aspiration — it is an urgent present need. The tools exist; what is required now is methodological adaptation, institutional investment, and genuine knowledge co-production with LMIC partners that will make those tools work where they are needed most.
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