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Am J Epidemiol. 2018 Dec 21. doi: 10.1093/aje/kwy254. [Epub ahead of print]
Classification of Spatiotemporal Data for Epidemic Alert Systems: Monitoring Influenza-Like Illness in France.
Polyakov P1, Souty C2, B?elle PY2, Breban R1.
Author information
Abstract
Surveillance data serving for epidemic alert systems are typically fully aggregated in space at the national level. 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 (1985-2016) 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.
PMID: 30576414 DOI: 10.1093/aje/kwy254
Classification of Spatiotemporal Data for Epidemic Alert Systems: Monitoring Influenza-Like Illness in France.
Polyakov P1, Souty C2, B?elle PY2, Breban R1.
Author information
Abstract
Surveillance data serving for epidemic alert systems are typically fully aggregated in space at the national level. 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 (1985-2016) 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.
PMID: 30576414 DOI: 10.1093/aje/kwy254