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Activity Number: 464 - SPEED: Infectious Diseases, Spatial Modeling and Environmental Exposures, Speed 1
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #304535
Title: Detecting Hierarchical Geographical Clusters of Disease Using Heterogeneity Patterns of Varying Incidence Intensity
Author(s): Chih-Chieh Wu* and Sanjay Shete
Companies: National Cheng Kung University and UT MD Anderson Cancer Center
Keywords: Spatial cluster; Temporal cluster; Hierarchical ; Dengue

We propose to perform a systematic investigation to detect and recognize spatial and temporal clustering patterns of dengue incidence in geographical areas of Taiwan. The largest dengue outbreaks in Taiwan since World War II occurred in two recent successive years: 2014 and 2015. We aim not only to examine whether or not spatial clustering of dengue incidence exists, but also to recognize geographical clustering of dengue incidence in a hierarchical manner. We extend the use of the ordinary map-based pattern recognition procedure and spatial scan statistic for the spatial and space-time analysis. We identify and delineate two separate hierarchical dengue incidence intensity clusters that comprise multiple mutually adjacent geographical units with high dengue incidence rates, based on the map-based pattern recognition procedure. We also find that that dengue incidence tends to peak simultaneously and homogeneously among the neighboring geographic units with high rates in the same cluster. Beyond significance testing, this study is particularly desired by and useful for health authorities who require optimal characteristics of disease incidence patterns on maps and over time.

Authors who are presenting talks have a * after their name.

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