Temporal analysis consists in identifying abnormal events in the temporal distribution of a disease. This can be straightforward for rare diseases requiring immediate notification, but often requires the use of statistical methods to differentiate abnormal events from the expected fluctuation in notifications for diseases occurring at a baseline level in the community.
Such statistical methods include, from the simplest to the most sophisticated:
Figure 1: Using a pre-defined threshold to detect unexpected changes
Figure 2: Using a Poisson test to detect unexpected changes
Figure 3: Using historical median and percentiles to detect unexpected changes
Figure 4: Using periodic regression modeling to detect unexpected changes
Figure 5: Using SARIMA modeling to detect unexpected changes
The selection of the most appropriate method for analysis of time characteristic must take into account: