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    Home » News » Wastewater monitoring could predict influenza outbreaks a week in advance
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    Wastewater monitoring could predict influenza outbreaks a week in advance

    healthadminBy healthadminMay 14, 2026No Comments3 Mins Read
    Wastewater monitoring could predict influenza outbreaks a week in advance
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    Seasonal influenza can spread rapidly, and timely information about rising numbers of cases is essential for public health decisions and health resource planning. However, traditional surveillance based on reported cases relies on health care-seeking behavior, laboratory tests, and reporting processes, which can lag real-world infection trends.

    A research team led by Professor Michio Murakami of Osaka University has demonstrated that measuring influenza virus RNA in wastewater can be used to estimate community influenza incidence. This approach could help identify trends in the epidemic about a week earlier than publicly available patient-reported data.

    This research Water and Environment Journalpresent a sewage-based epidemiology approach to estimate influenza incidence not only overall but also separately for influenza A and influenza B. This type-specific estimation could support more detailed monitoring of seasonal outbreaks.

    The researchers analyzed weekly sewage samples collected from three sewage treatment plants in Osaka Prefecture from April 2023 to April 2025. They measured RNA concentrations of influenza A and B viruses and combined these data with infectious disease surveillance data to build a statistical model to predict influenza cases.

    Results showed that the model predicted the overall incidence of influenza A+B with high accuracy during both the model development and validation periods. This approach also made it possible to estimate trends for influenza A and influenza B separately, but the researchers note that further validation is needed when different influenza A subtypes and influenza B strains are circulating.

    The main advantage of wastewater monitoring is timeliness. Although measurements of viral RNA in wastewater could theoretically be obtained within 1 to 2 days after sampling, clinical influenza case data are typically published approximately 1 week later. This means that wastewater-based estimates have the potential to provide public health authorities with earlier information about changes in incidence trends.

    Wastewater surveillance can help reflect infection activity in the community even when laboratory testing is limited. In this study, influenza A virus RNA was detected in wastewater during non-endemic periods, suggesting that wastewater signals may capture infections that are not fully reflected by patient-based surveillance.

    “We found that by measuring influenza viruses in wastewater, we can estimate the incidence of community-acquired influenza by type, dividing it into influenza A and B, about a week earlier than publicly available patient-reported data. This paper reports results up to April 2025, but monitoring has been ongoing since then. “We have confirmed that the model continues to estimate outbreak trends with high accuracy. We hope that these findings will help health systems prepare early, such as by securing hospital beds in anticipation of an increase in the number of hospitalizations due to an increase in influenza cases,” said Michio Murakami, lead author of the paper. Study.

    The findings suggest that wastewater monitoring can complement traditional influenza surveillance and support early data-driven preparedness. Early detection of outbreak trends can help health care providers and public health officials make timely decisions about bed allocation, staffing, and other health care resources. This approach could be extended to other infectious diseases and could contribute to the development of real-time surveillance systems at the regional level.

    sauce:

    Osaka University

    Reference magazines:

    Early prediction of type-specific influenza incidence using wastewater-based epidemiology. Water and Environment Journal. DOI: https://doi.org/10.1111/wej.70060



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