📊 epidemiology

Wastewater Treatment Plants as Representative Sentinel Sites in Infectious Disease Surveillance

This study demonstrates that wastewater treatment plants effectively serve as sentinel sites for monitoring infectious disease dynamics in surrounding off-network populations, with mobility-defined communities offering a superior framework for assessing representativeness compared to traditional administrative boundaries.

Original authors: Fiatsonu, E., Hill, D., Christopher, D., Larsen, D.

Published 2026-08-31

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Fiatsonu, E., Hill, D., Christopher, D., Larsen, D.

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ⚕️ This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer

Public health officials have long relied on clinical testing to track the spread of infectious diseases, counting positive cases in hospitals and clinics to understand where a virus is moving. However, this approach has a blind spot: it only sees people who get tested, and it struggles to reach communities without easy access to healthcare or testing sites. To fill this gap, scientists have turned to wastewater-based epidemiology, a method that treats sewage as a massive, passive sampling tool. Instead of testing individuals one by one, researchers analyze the water flowing out of treatment plants to detect the genetic material of viruses shed by an entire community. This technique has proven remarkably effective at spotting outbreaks early, often before clinical cases are reported, because the virus travels through pipes from homes to the treatment facility regardless of whether the infected person feels sick or seeks medical care. Yet, a critical question remained unanswered for years: does this method work for people who do not live in areas connected to the sewer system? In many places, particularly rural ones, residents rely on septic tanks rather than central pipes, meaning their waste never reaches the treatment plants scientists monitor. If the virus spreads in these unconnected areas, the wastewater signal might miss it entirely, leaving a dangerous gap in the nation's early-warning system.

Researchers at Syracuse University set out to solve this puzzle by examining how human movement connects sewered and unsewered populations. They focused on New York State, excluding New York City, to see if the wastewater from treatment plants could accurately reflect the disease trends of people living outside the pipe network. The team gathered daily data on COVID-19 tests and positive cases from January 2021 through April 2022, covering a period that included the Delta and Omicron waves. They divided the population into two groups: those living in areas served by large wastewater treatment plants, which they called "in-network," and those living outside these systems, known as "off-network." To make the analysis more accurate, they did not just look at official county lines, which are drawn for government purposes and often split communities that interact daily. Instead, they used data from mobile devices to map how people actually moved between their homes, workplaces, and shops, grouping these movements into "mobility-defined communities" that reflect real-world social connections.

The study found that the wastewater signals were far more representative of the off-network population than anyone had previously assumed. Because people routinely travel between towns and rural areas for work, school, and social activities, they carry the virus with them, mixing the disease dynamics of connected and unconnected areas. When the researchers compared the trends in wastewater samples to the clinical test results, they discovered a striking alignment. At the statewide level, the rise and fall of infections in the sewered areas matched the trends in the unsewered areas almost perfectly, with a correlation so strong it was nearly identical. Even when they looked at individual counties and the smaller, mobility-defined communities, the connection held up. In most cases, the wastewater data from a treatment plant served as a reliable mirror for the entire surrounding region, including the rural residents whose waste never entered the pipes. This suggests that the virus does not respect the boundaries of sewer infrastructure; as long as people move between areas, the wastewater signal captures the broader epidemic.

However, the researchers also identified where this system begins to break down. The strength of the connection depended heavily on the size of the community. In larger, more populated areas, the mixing of people was sufficient to keep the disease trends synchronized, making the wastewater a robust sentinel for everyone. In smaller, more rural communities, the link was weaker. In these places, the population was often too small to generate a stable signal, and the physical distance between the town centers with sewers and the surrounding rural homes meant that local outbreaks could happen in isolation without immediately affecting the wastewater sample. The study revealed that about 42 percent of the mobility-defined communities in the state had no wastewater treatment plant coverage at all, representing a significant surveillance gap. While the existing network effectively monitored the vast majority of the state's population, these isolated rural pockets remained vulnerable to being missed by the early-warning system.

The findings challenge the idea that wastewater surveillance is only useful for the people who live directly above the pipes. The research demonstrates that routine human movement acts as a bridge, allowing the wastewater from a treatment plant to tell the story of disease spread in the surrounding countryside. This means that current monitoring systems are already providing a level of protection to rural populations that was not fully appreciated. Yet, the study also argues that relying solely on this indirect connection is not enough for the most isolated communities. Because the signal weakens in small towns, the authors suggest that expanding wastewater surveillance to include smaller, rural treatment facilities is a necessary step toward health equity. By ensuring that every community, regardless of its size or infrastructure, has a dedicated early-warning system, public health officials can ensure that no population is left in the dark when a new threat emerges. The work confirms that while the pipes do not reach everyone, the people do, but for the most remote areas, the pipes themselves must eventually be extended to keep the whole system safe.

Technical Summary: Wastewater Treatment Plants as Representative Sentinel Sites in Infectious Disease Surveillance

Problem Statement
Wastewater-based epidemiology (WBE) has proven effective for population-level surveillance of infectious diseases, particularly SARS-CoV-2. However, existing surveillance infrastructure is structurally concentrated in urban, sewered ("in-network") areas, potentially leaving rural and peri-urban populations ("off-network") who rely on decentralized systems (e.g., septic tanks) underrepresented. A critical unresolved question is whether wastewater treatment plant (WWTP) measurements can accurately reflect infectious disease dynamics in these surrounding off-network communities. While mechanistic modeling suggests human mobility might bridge this gap, empirical evidence quantifying the alignment between in-network wastewater signals and off-network clinical trends, particularly when accounting for functional mobility patterns rather than static administrative boundaries, remains limited.

Methodology
The study utilized daily clinical COVID-19 testing data from New York State (excluding New York City) spanning January 2021 to April 2022. The analysis employed two distinct geographic frameworks to evaluate the correlation between in-network and off-network disease metrics:

Administrative County Framework: Data was aggregated across 57 counties. Populations were classified as "in-network" (residing within the catchment of WWTPs with ≥1.0 million gallons/day capacity) or "off-network" (within the county but outside these catchments).
Mobility-Defined Community Framework: To capture functional population connectivity, the authors applied the Walktrap community detection algorithm to census tract-level human mobility networks derived from anonymized mobile device location data. This approach identified 66 mobility-defined communities, from which 2 Seneca Nation tracts were excluded due to unique testing protocols, resulting in a final analytical sample of 64 communities. Within these functional units, populations were similarly stratified into in-network and off-network strata.

The study analyzed weekly incidence rates and test positivity for both strata. Correlations were assessed using Pearson coefficients and cross-correlation functions to determine temporal alignment. Additionally, weighted linear regression models were fitted to identify predictors of epidemiological alignment (e.g., population size, sewered fraction, WWTP density, signal variability). Finally, the study correlated weekly SARS-CoV-2 wastewater RNA concentrations with clinical indicators in both frameworks to determine if wastewater signals were preferentially informative for the directly sampled in-network population or the broader mobility community.

Key Results

High Epidemiological Alignment: At the statewide aggregate level, in-network and off-network trends were strongly correlated for both incidence ( $r=0.994$ ) and test positivity ( $r=0.996$ ) under the county framework, and similarly strong under the mobility-defined framework ( $r=0.990$ and $r=0.992$ , respectively). Cross-correlation analysis confirmed synchronous dynamics with no significant time lag (peak at lag 0).
Unit-Level Variability: While statewide aggregates showed high correlation, individual unit correlations varied. The median correlation for counties was $0.909$ (incidence) and $0.932$ (positivity). For mobility-defined communities, medians were slightly lower at $0.877$ and $0.894$, respectively.
Predictors of Alignment: Population size was the dominant predictor of in-network/off-network alignment at both scales. At the mobility-defined community level, WWTP density and off-network signal variability provided additional explanatory power for test positivity, whereas these factors were not significant at the county level.
Framework Comparison: The mobility-defined community framework offered a more balanced population split between in-network and off-network strata (87.5% of units in the balanced range vs. 69.8% for counties) and established a higher "floor" for representativeness (minimum correlation $r=0.440$ vs. $0.177$ for counties). Crucially, this framework revealed that 42% of mobility-defined communities had no WWTP coverage at all, a surveillance gap less apparent when analyzing data solely by county.
Wastewater Signal Representativeness: Paired Wilcoxon signed-rank tests revealed no significant difference between the correlation of wastewater concentrations with in-network versus off-network clinical burden. This indicates that wastewater signals are not preferentially informative for the directly sampled population but reflect the broader mobility community.

Key Contributions

Empirical Validation of Sentinel Sites: The study provides robust empirical evidence that WWTPs serve as representative sentinel sites for surrounding off-network populations, driven by routine human movement that transcends sewer infrastructure boundaries.
Methodological Advancement: By integrating mobility-defined communities, the research demonstrates that functional population units provide a more equitable and accurate analytical framework for surveillance than static administrative boundaries, particularly in identifying rural surveillance gaps.
Equity Assessment: The analysis quantifies the "surveillance equity" gap, highlighting that while existing infrastructure covers the majority of the population, a significant portion of rural communities (42% of mobility-defined communities) lack any wastewater surveillance coverage, despite the representativeness of existing sites.

Significance and Claims
The paper claims that wastewater surveillance can effectively monitor infectious diseases in populations beyond the limits of the sewer network, provided there is sufficient human mobility connectivity. The authors assert that existing WBE infrastructure in New York State broadly captures disease dynamics for both connected and unconnected populations, supporting the use of WWTPs as sentinel sites for the entire mobility community.

However, the authors maintain a modest stance regarding the limitations of this representativeness. They note that alignment weakens in smaller, more rural communities where geographic separation and differential access to testing may lead to divergent epidemic trajectories. Consequently, while current infrastructure is operationally sufficient for most of the state, the absence of physical infrastructure in 42% of mobility-defined communities represents a critical surveillance gap. The paper concludes that expanding wastewater surveillance to rural facilities should be treated as a priority for surveillance equity to ensure early-warning capabilities are available to all communities, rather than relying solely on the representativeness of existing urban-centric sites.

Technical Summary: Wastewater Treatment Plants as Representative Sentinel Sites in Infectious Disease Surveillance

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