Epidemiological surveillance of emerging viral diseases through wastewater monitoring has become established as a non-invasive, low-cost approach for investigating pathogen circulation in populations. This method is based on detecting viral genomes excreted in feces, allowing early population-level outbreak tracking independent of active healthcare seeking. This study aims to demonstrate the potential of a universal, low-cost, high-efficacy method for monitoring influenza A (IAV) in wastewater in the city of Porto Alegre (RS). Twenty-seven raw sewage samples from the Serraria Wastewater Treatment Plant, which serves half of the local population, were collected from January to April 2025 (epidemiological weeks (EW) 1 to 17). Samples were concentrated by ultracentrifugation, and total RNA extraction was performed using the RSC Viral Total Nucleic Acid kit. RT-qPCR targeted the IAV M gene. During the COVID-19 pandemic, wastewater monitoring proved effective for detecting SARS-CoV-2, often preceding increases in confirmed clinical cases by up to two weeks, since infected individuals shed virus before symptom onset or clinical diagnosis. Here, three of the 27 samples were positive for IAV: one in EW 16 and two in EW 17. The city Health Department identified increases in IAV cases and hospitalizations during EW 18 and 19, validating this methodology as an early warning system for IAV that detected viral genetic material in wastewater before observed epidemiological peaks. Beyond predictive capacity, this surveillance model also enables indirect inferences about vaccine effectiveness and coverage. In populations with high vaccination rates, a reduction in environmental viral load is expected, reflecting lower community transmission. Persistence of pathogens in wastewater even after vaccination campaigns may indicate coverage gaps, variant emergence, or reduced vaccine effectiveness. Continuous monitoring can therefore reveal the need for booster doses or new immunization strategies. In resource-limited contexts, environmental monitoring is an effective complementary approach to traditional surveillance, supporting faster, more equitable, evidence-based public health decisions.
Stone et al. (2026) studied this question.