We experimented with a range of popular models for early alerting used in the public health community: the Early Aberration and Reporting System (EARS) quality control chart models C3, C2 and W2 as well as the F-statistic and the Exponential Weighted Moving Average (EWMA). All were implemented in Excel for the purpose of this study. The models are what might be termed 'snapshot' models because they all use short 7 day baselines that assume a relatively stationary background, i.e. ignoring medium to long term periodic variations such as seasonal cycles. The baselines are used to predict future trends against which the current day values are compared. All models also use a 2 day 'guard period' just before the target day t to prevent the current day's data from being included in the baseline. All models use a minimally supervised method by setting a threshold parameter which we determined using the same 5 held out data sets used by [4] . These were 0.2 (C2 and W2), 0.3 (C3), 0.6 (F-statistic) and 2.0 (EWMA). A minimum standard deviation was set at 0.2 and a frequency purge was applied to remove singleton events, i.e. those with counts of 1 per day.