Evaluation of the Automated and Early Detection of Seasonal Epidemic Onset and Burden Levels (AEDSEO) method for respiratory surveillance using data from 21 European countries

BACKGROUNDSeasonal respiratory pathogens place recurrent pressure on health services, creating a need for timely detection of seasonal onset and assessment of within-season intensity.AIMWe aimed to evaluate the Automated and Early Detection of Seasonal Epidemic Onset and Burden Levels (AEDSEO) method, developed using Danish respiratory surveillance data, externally.METHODSUsing 63 surveillance series of influenza, respiratory syncytial virus (RSV), acute respiratory infection (ARI) and influenza-like illness (ILI) from 21 European countries, season onset detection was compared with the Moving Epidemic Method (MEM), while intensity level categorisation was compared with MEM, the World Health Organization Average Curve Method (WHO-ACM) and the Mean Standard Deviation method (MSD).RESULTSThe AEDSEO method signalled onset in 60 of 63 surveillance series, while MEM crossed its epidemic threshold in 55 of 63. Among 55 surveillance series in which both methods signalled onset, AEDSEO signalled earlier in 45. Median lead times were 6.5 weeks for influenza, 4.5 for RSV, 22.0 for ARI and 5.5 for ILI. Growth after the AEDSEO onset signal was usually sustained, particularly for influenza, RSV and ILI. The AEDSEO method provided more consistent within-season intensity categorisation across seasons than MEM, WHO-ACM and MSD.CONCLUSIONAcross diverse European respiratory surveillance series AEDSEO supported earlier detection of seasonal onset than MEM and more consistent within-season intensity categorisation than MEM, WHO-ACM and MSD. Its current operational use in the Danish national respiratory surveillance system further supports its practical applicability for timely situational assessment, planning, and communication of respiratory pathogen activity.