A review of several data sources used for syndromic influenza surveillance is given by Dailey et al. [1], discussing the strengths and weaknesses of the different data sources. Elaborated examples on the use of syndromic surveillance for early warning using school absenteeism data and ambulance dispatch data are given in Besculides et al. [2] and Bork et al. [3], respectively. One of the most important characteristics of a syndromic surveillance system is its timeliness, or the difference between the time of occurrence of a (syndromic) event and the time of occurrence of the reference standard. The latter is commonly chosen to be the influenza-like illness incidence as monitored through the SGP networks in Europe [4]

The collection of the school absenteeism data started only some weeks before (German-speaking Community) or even after the onset of the epidemic (Flemish and French Community). Nonetheless, some interesting observations can still be made. Although the main interest is in predicting the onset of the influenza epidemic, it is promising to see that the peaks of the primary and secondary school absenteeism rates preceded the ILI peak with ± 2 weeks. This finding is only partially confirmed by the nursery school absenteeism rates. For the German-speaking and French Communities, a first peak in nursery absenteeism was observed ± 1.5 weeks prior to the ILI peak and a second (strong) rise was observed from the beginning of December until the start of the Christmas holidays. The latter rise might be explained by a strong increase in circulation of the Respiratory Syncytial Virus (RSV), which affects predominantly young children. Indeed, the Sentinel Laboratory network coordinated by the WIV-ISP, confirmed a strong rise in RSV-positive samples since week 41 with a peak at week 50. For the Flemish Community, a rise in nursery absenteeism rates was observed from the beginning of the data collection until the start of the Christmas holidays.

Since the preliminary results are promising, it seems worthwhile to collect absenteeism data on longer time periods covering several influenza epidemics. Once such data are available, early warning algorithms can be developed taking into account e.g. seasonality, day-of-week effect, holiday effect and spatial variability. Evidently, the accuracy (i.e. the false positive and false negative rate) of such an early warning system needs to be scrutinised before implementation. In particular, high false positive rates are to be expected because several causes of increased absenteeism rates exist, especially for the younger age groups. However, by developing an early warning system that uses different data sources simultaneously, the accuracy is expected to improve sub-stantially. Nevertheless, developing early warning systems based on absenteeism data has several advantages as well. First, because absenteeism data (like other 'syndromic' data sources) are not disease-specific, they can be used to detect a variety of circulating diseases other than influenza. Second, absenteeism data (like other 'syndromic' data sources) are prediagnostic, implying a gain in time compared to diagnostic data sources (e.g. laboratory-confirmed influenza cases). Third, and in contrast to e.g. 'syndromic' surveillance using hospitalisa-tion or mortality data, absenteeism surveillance can potentially detect outbreaks of diseases causing minor illnesses. A fourth advantage of (school) absenteeism surveillance is that it allows monitoring age-specific sub-populations. Indeed, by monitoring nursery school absenteeism rates, a strong increase in RSV-circulation was detected, which would likely have remained unnoticed when using non-age-specific data sources. Finally, be-cause absenteeism surveillance (like other 'syndromic' data sources) makes use of existing data, it is relatively economically beneficial and easy to collect the data. In particular, for the worker absenteeism surveillance, existing data files are sent to and analysed by the WIV-ISP. For the school absenteeism surveillance, schools summarise the collected data, report the summary data electronically in a database hosted by the corresponding authorities. Thereafter the data, aggregated over schools, are sent for analysis and reporting to the WIV-ISP.