Near real-time space-time cluster analysis for detection of enteric disease outbreaks in a community setting

作者：Glatman Freedman Aharona^*; Kaufman Zalman; Kopel Eran; Bassal Ravit; Taran Diana; Valinsky Lea; Agmon Vered; Shpriz Manor; Cohen Daniel; Anis Emilia; Shohat Tamy

来源：Journal of Infection, 2016, 73(2): 99-106.

DOI：10.1016/j.jinf.2016.04.038

• Cluster analysis
• Public health
• Enteric pathogens
• Outbreak detection
• Geographical information system (GIS)

Objectives: To enhance timely surveillance of bacterial enteric pathogens, space-time cluster analysis was introduced in Israel in May 2013.
Methods: Stool isolation data of Salmonella, Shigella, and Campylobacter from patients of a large Health Maintenance Organization were analyzed weekly by ArcGIS and SaTScan, and cluster results were sent promptly to local departments of health (LDOHs).
Results: During eighteen months, we identified 52 Shigella sonnei clusters, two Salmonella clusters, and no Campylobacter clusters. S. sonnei clusters lasted from one to 33 days and included three to 30 individuals. Thirty-one (60%) of the S. sonnei clusters were known to LDOHs prior to cluster analysis. Clusters not previously known by the LDOHs prompted epidemiologic investigations. In 31 of the 37 (84%) confirmed clusters, educational institutes (nursery schools, kindergartens, and a primary school) were involved.
Conclusions: Cluster analysis demonstrated capability to complement enteric disease surveillance. Scaling up the system can further enhance timely detection and control of outbreaks.

• 出版日期2016-8