Prospective Spatio-Temporal and Temporal Cluster Detection by Salmonella Serotype | Zendy

Eric Peterson | Zendy; Vasudha Reddy | Zendy; HaeNa Waechter | Zendy; Lan Li | Zendy; Kristen Forney | Zendy; Sharon K. Greene | Zendy

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Prospective Spatio-Temporal and Temporal Cluster Detection by Salmonella Serotype

Author(s) -

Eric Peterson,

Vasudha Reddy,

HaeNa Waechter,

Lan Li,

Kristen Forney,

Sharon K. Greene

Publication year - 2016

Publication title -

online journal of public health informatics

Language(s) - English

Resource type - Journals

ISSN - 1947-2579

DOI - 10.5210/ojphi.v8i1.6443

Subject(s) - scan statistic , salmonella , serotype , cluster (spacecraft) , computer science , statistic , geography , data mining , biology , virology , statistics , mathematics , genetics , bacteria , programming language

To improve (Salmonella) cluster detection by serotype in New York City (NYC), we developed an automated daily process to assign serotypes to (Salmonella) cases. We implemented daily analyses using the prospective space-time permutation scan statistic in SaTScan to detect spatio-tempOral and purely tempOral clusters. In 14 weeks of spatio-tempOral analyses, 7 clusters were identified, and in 4 weeks of purely tempOral analyses, 5 clusters were identified. These methods are useful complements to the NYC (Salmonella) surveillance system and could be adopted by other health departments for primary or confirmatory cluster detection.

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