What Different Surveillance Systems Tell Us About Winter Respiratory Illness in England: A Time-Series Analysis Combining Participatory, Syndromic and Laboratory Datasets, 2022-2025.

BACKGROUND: A range of surveillance systems have been employed in England to monitor respiratory illness, including participatory, syndromic and laboratory datasets. Combined, they can provide powerful insights on the spectrum of respiratory illness across the population. We compared trends across these datasets over three successive winters. METHODS: Participatory, syndromic and laboratory surveillance datasets from the UK Health Security Agency were integrated. Winter trends in influenza-like illness (ILI), using a standardised definition, were described and compared across datasets, 2022/2023 to 2024/2025, using the cross-correlation function (CCF). Multivariable (beta and negative binomial) regression models were used to explore the association between ILI and SARS-CoV-2, influenza and RSV positivity in each dataset. RESULTS: Trends in ILI align well across participatory surveillance systems, with ILI peaking around Weeks 50-52 each season. Participatory surveillance system trends align well with syndromic healthcare use for ILI, with ILI symptoms peaking about a week before healthcare use. ILI in each dataset is driven by a different combination of respiratory viruses, with ILI in the community driven by a combination of SARS-CoV-2, influenza and RSV, whereas healthcare attendance was driven by influenza only. CONCLUSIONS: While each surveillance system differs in its purpose and population, trends in ILI align across datasets. Comparing a range of surveillance systems can provide increased confidence in each system. While syndromic healthcare use surveillance systems highlight the burden of severe illness, integration with participatory surveillance systems can demonstrate the wider burden across a spectrum of severity.