Recognizing spatial and temporal clustering patterns of dengue outbreaks in Taiwan

Abstract

BackgroundDengue fever is the most common arboviral infection in humans, with viral transmissions occurring in more than 100 countries in tropical regions. A global strategy for dengue prevention and control was established more than 10 years ago. However, the factors that drive the transmission of the dengue virus and subsequent viral infection continue unabated. The largest dengue outbreaks in Taiwan since World War II occurred in two recent successive years: 2014 and 2015.MethodsWe performed a systematic analysis to detect and recognize spatial and temporal clustering patterns of dengue incidence in geographical areas of Taiwan, using the map-based pattern recognition procedure and scan test. Our aim was to recognize geographical heterogeneity patterns of varying dengue incidence intensity and detect hierarchical incidence intensity clusters.ResultsUsing the map-based pattern recognition procedure, we identified and delineated two separate hierarchical dengue incidence intensity clusters that comprise multiple mutually adjacent geographical units with high dengue incidence rates. We also found that that dengue incidence tends to peak simultaneously and homogeneously among the neighboring geographic units with high rates in the same cluster.ConclusionBeyond 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. Among the integrated components for effective prevention and control of dengue and dengue hemorrhagic fever are active surveillance and community-based integrated mosquito control, for which this study provides valuable inferences. Effective dengue prevention and control programs in Taiwan are critical, and have the added benefit of controlling the potential emergence of Zika.Electronic supplementary materialThe online version of this article (10.1186/s12879-018-3159-9) contains supplementary material, which is available to authorized users.

Differentiating anomalous disease intensity with confounding variables in space

“…It is noted that this procedure exclusively focuses on peak incidence and does not allow for covariates in determining the hierarchical intensity levels. We previously used the pattern recognition procedure to investigate the spatial clustering patterns of dengue outbreaks in Taiwan [10].…”

“…Each of the 24 distributions of B 1 for k = 2, 3, 4… 25 is given in Table 1. We also used simulation-based permutations using 1 million replicates based on the exact district boundary map under study to obtain the null distribution of B 1 in a study of dengue fever in Taiwan previously [10].…”

Choropleth Map Legend Design for Visualizing the Most Influential Areas on the Topic of Anesthesiology: Bibliometric Analyses

“…All legends in CMs consist of the three types of information including (1) the cutting points, (2) the cumulative frequency on total values in classes, and (3) the observation counts across classes, which is unique when compared to the traditional CMS, such as dengue outbreaks [4,5], disease hotspots [6], and the Global Health Observatory (GHO) maps on major health topics [7], only with the cutting points using the classification method of equal intervals. Although several legends were proposed before [9][10][11][12], such as ogive-based legends, proportional symbols and gradual bubbles.…”

A novel approach to the classification of performance on inpatient perception of hospitalization experience across the US 52 states

“…Furthermore, many examples of disparities in health outcomes across areas, such as dengue outbreaks [31,32], disease hotspots [33], and the Global Health Observatory (GHO) maps on major health topics [34], have been presented using choropleth maps [23][24][25] to display. Our representation in Figures 1 and 2 on Google Maps is unique and innovative and has no precedent in the literature.…”