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Identification of geographic clusters for temporal heterogeneity with application to dengue surveillance
Author(s) -
Lin PeiSheng
Publication year - 2021
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.9227
Subject(s) - scan statistic , dengue fever , statistic , computer science , cluster analysis , similarity (geometry) , identification (biology) , data mining , cluster (spacecraft) , statistics , artificial intelligence , mathematics , medicine , botany , image (mathematics) , biology , programming language , immunology
Identifying transmission of hot spots with temporal trends is important for reducing infectious disease propagation. Cluster analysis is a particularly useful tool to explore underlying stochastic processes between observations by grouping items into categories by their similarity. In a study of epidemic propagation, clustering geographic regions that have similar time series could help researchers track diffusion routes from a common source of an infectious disease. In this article, we propose a two‐stage scan statistic to classify regions into various geographic clusters by their temporal heterogeneity. The proposed scan statistic is more flexible than traditional methods in that contiguous and nonproximate regions with similar temporal patterns can be identified simultaneously. A simulation study and data analysis for a dengue fever infection are also presented for illustration.

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