Findings are expected to support early warning systems, guide public health actions, and accelerate the development of more adaptable vaccines.
Monitoring the influenza virus through wastewater samples has enabled the Institut Pasteur de São Paulo (IPSP) to anticipate, by approximately two weeks, increases in disease cases in the city of São Paulo—an outcome that could transform how health authorities prepare for outbreaks and future pandemics.
Coordinated by virologist and immunologist Rúbens Alves, the project integrates environmental surveillance, molecular analysis, and data modeling to map, with high sensitivity, virus circulation within the population—including asymptomatic cases that do not reach the healthcare system. This approach does not replace but complements traditional epidemiological surveillance systems, expanding the capacity to detect and anticipate outbreaks.
“We are able to predict increases in flu cases two weeks in advance based on the viral load detected in sewage. This opens the way for much more efficient early warning systems,” the researcher explains.
Viral circulation – The monitoring principle is straightforward, but its scope is broad. Infected individuals shed viral genetic material in secretions, feces, and urine, which ultimately reach the sewage system—often before symptoms appear. By collecting and analyzing wastewater samples from different regions of the city, researchers can identify not only the presence of the virus but also the intensity of its circulation.
In practice, the project currently operates with 10 sampling points distributed across different regions of the capital, covering central areas as well as the northern, southern, and eastern zones, in addition to strategic locations such as Guarulhos Airport. This capillarity allows for a representative sample of different urban dynamics.
Sampling is carried out with the support of CETESB at treatment plants, pumping stations, and urban network points, following protocols tailored to each type of environment. Once in the laboratory, samples undergo a concentration process, as the amount of virus present in sewage is very small relative to total volume. This is followed by genetic material extraction and viral load quantification.
This last step is crucial. Beyond detecting the presence of the virus, the method enables tracking its variation over time, indicating whether circulation is increasing or decreasing in specific regions.
“We are not just interested in whether the virus is present, but whether it is rising or falling. It is this dynamic that allows us to predict outbreaks,” says Alves.
Critical window – By comparing wastewater data with clinical records available in public databases, researchers identified a consistent pattern: increases in viral load in sewage precede, by about two weeks, the rise in more severe cases of the disease.
This lead time creates a strategic intervention window. Even before the healthcare system registers an increase in patients, it becomes possible to identify regions with higher viral circulation and direct preventive measures accordingly. The logic shifts from reactive to anticipatory.
Unlike traditional surveillance, which depends on individuals seeking medical care, wastewater monitoring also captures mild or asymptomatic infections. This significantly enhances detection capacity and makes the system more sensitive to changes in transmission dynamics.
Urban system – Data generated throughout the project have begun to reveal more sophisticated patterns of viral circulation across the city. The distribution of cases is not homogeneous and varies over time, with some regions showing persistent viral circulation and others exhibiting distinct behaviors.
These differences appear to reflect a combination of factors beyond viral biology, including urban mobility, access to healthcare services, hospital distribution, and socioeconomic characteristics.
To address this complexity, the project is advancing to a new phase by incorporating artificial intelligence in partnership with Insper. The goal is to integrate multiple layers of information and build models capable of identifying broader patterns and predicting spread dynamics with greater precision.
“Today we work with simpler correlations. When we incorporate additional variables, we begin to better understand how the city functions and how the virus spreads within it” Alves explains.
Expanded perspective – Another axis of the project involves expanding the datasets used in the analysis. In addition to public healthcare system data, researchers have begun incorporating information from different care segments, including networks such as Dr. Consulta, which serve a significant portion of the population outside both the traditional public system and high-complexity private hospitals. This intermediate layer—often invisible in epidemiological analyses—contributes to a more comprehensive understanding of viral circulation.
The integration of these different sources reduces distortions and gaps typical of surveillance based on a single dataset, bringing the analysis closer to a more accurate representation of the city’s epidemiological reality.
“Each dataset shows part of the population. When we integrate this information, we begin to see the system as a whole,” the researcher explains.
From surveillance to vaccines – In parallel with monitoring, the project is also advancing the development of a new influenza vaccine platform. The proposal is based on self-replicating RNA, a technology that enables longer-lasting immune responses with smaller amounts of material and faster production.
The goal is to overcome one of the main limitations of current vaccines, which rely on forecasts made months in advance and do not always match circulating strains. By integrating surveillance with technological development, the aim is to reduce the time between identifying new variants and updating the vaccine.
“The idea is to connect these two fronts. The faster we identify circulating strains, the faster we can respond with an adapted vaccine,” says Alves.
Although this stage is still in its early phases—dependent on experimental infrastructure and validation in biological models—it is considered strategic within the project.
Future preparedness – Influenza remains one of the main threats in terms of future pandemics. Its capacity for mutation and genetic recombination, combined with circulation across different species, creates a persistent risk scenario.
Over the past century, different variants of the virus have been responsible for major global pandemics, and experts expect similar events to occur again.
In this context, the IPSP project is part of a preparedness agenda. Beyond monitoring seasonal outbreaks, the initiative seeks to develop tools capable of early detection of changes in viral behavior and enabling faster responses.
“If a more aggressive virus emerges, we need to be ready to quickly identify where it is circulating and act before it spreads,” Alves states.
Beyond influenza – Although the current focus is on the flu virus, the approach developed by IPSP has the potential to be applied to other pathogens. Wastewater monitoring has already proven effective during the COVID-19 pandemic and can be expanded to different agents of public health interest.
The partnership with CETESB—which has a strong track record in environmental surveillance and accumulated experience in virus monitoring in wastewater—is one of the pillars of this expansion.
The expectation is that, in the coming years, the system will evolve into a continuous urban epidemiological surveillance platform, capable of integrating multiple data sources and providing a more dynamic understanding of population health.