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Surveillance data serving for epidemic alert systems are typically fullyaggregated in space. However, epidemics may be spatially heterogeneous,undergoing distinct dynamics in distinct regions of the surveillance area. Weunveil this in retrospective analyses by classifying incidence time series. Weuse Pearson correlation to quantify the similarity between local time seriesand then classify them using modularity maximization. The surveillance area isthus divided into regions with different incidence patterns. We analyzed 31years of data on influenza-like-illness from the French system Sentinelles andfound spatial heterogeneity in 19/31 influenza seasons. However, distinctepidemic 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 administrativeregions were experiencing an epidemic, while the others were not. Second, thenationwide alert was timely for the whole surveillance area, but, subsequently,regions experienced distinct epidemic dynamics. Third, the epidemic dynamicswere homogeneous in space. Spatial heterogeneity analyses can provide thetiming of the epidemic peak and finish, in various regions, to tailor diseasemonitoring and control.

Spatial heterogeneity analyses identify limitations of epidemic alert systems: Monitoring influenza-like illness in France