  Surveillance data serving for epidemic alert systems are typically fully
aggregated in space. However, epidemics may be spatially heterogeneous,
undergoing distinct dynamics in distinct regions of the surveillance area. We
unveil this in retrospective analyses by classifying incidence time series. We
use Pearson correlation to quantify the similarity between local time series
and then classify them using modularity maximization. The surveillance area is
thus divided into regions with different incidence patterns. We analyzed 31
years of data on influenza-like-illness from the French system Sentinelles and
found spatial heterogeneity in 19/31 influenza seasons. However, distinct
epidemic regions could be identified only 4-5 weeks after the nationwide alert.
The impact of spatial heterogeneity on influenza epidemiology was complex.
First, when the nationwide alert was triggered, 32-41% of the administrative
regions were experiencing an epidemic, while the others were not. Second, the
nationwide alert was timely for the whole surveillance area, but, subsequently,
regions experienced distinct epidemic dynamics. Third, the epidemic dynamics
were homogeneous in space. Spatial heterogeneity analyses can provide the
timing of the epidemic peak and finish, in various regions, to tailor disease
monitoring and control.
