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The record-breaking number of dengue cases reported in Guangdong, China in 2014 has been topic for many studies. However, the spatial and temporal characteristics of this unexpectedly explosive outbreak are still poorly understood. We adopt an integrated approach to ascertain the spatial-temporal progression of the outbreak in each city in Guangdong as well as in each district in Guangzhou, where the majority of cases occurred. We utilize the Richards model, which determines the waves of reported cases at each location and identifies the turning point for each wave, in combination with a spatial association analysis conducted by computing the standardized G* statistic that measures the degree of spatial autocorrelation of a set of geo-referenced data from a local perspective. We found that Yuexiu district in Guangzhou was the initial hot spot for the outbreak, subsequently spreading to its neighboring districts in Guangzhou and other cities in Guangdong province. Hospital isolation of cases during early stage of outbreak in neighboring Zhongshan (in effort to prevent disease transmission to the vectors) might have played an important role in the timely mitigation of the disease. Integration of modeling approach and spatial association analysis allows us to pinpoint waves that spread the disease to communities beyond the borders of the initially affected regions.

Urbanization is an important factor contributing to the global spread of dengue in recent decades, especially in tropical regions. However, the impact of public transportation system on local spread of dengue in urban settings remains poorly understood, due to the difficulty in collecting relevant locality, transportation and disease incidence data with sufficient detail, and in suitably quantifying the combined effect of proximity and passenger flow. We quantify proximity and passenger traffic data relating to 2014-2015 dengue outbreaks in Kaohsiung, Taiwan by introducing a "Risk Associated with Metro Passengers Presence" (RAMPP), which considers the passenger traffic of stations located within a fixed radius, giving more weight to the busier and/or closer stations. In order to analyze the contagion risk associated with nearby presence of one or more Kaohsiung Rapid Transit (KRT) stations, we cluster the Li's (the fourth level administrative subdivision in Taiwan) of Kaohsiung based on their RAMPP value using the K-means algorithm. We then perform analysis of variance on distinct clusterings and detect significant differences for both years. The subsequent post hoc tests (Dunn) show that yearly incidence rate observed in the areas with highest RAMPP values is always significantly greater than that recorded with smaller RAMPP values. RAMPP takes into account of population mobility in urban settings via the use of passenger traffic information of urban transportation system, that captures the simple but important idea that large amount of passenger flow in and out of a station can dramatically increase the contagion risk of dengue in the neighborhood. Our study provides a new perspective in identifying high-risk areas for transmissions and thus enhances our understanding of how public rapid transit system contributes to disease spread in densely populated urban areas, which could be useful in the design of more effective and timely intervention and control measures for future outbreaks., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Objective To investigate whether major dengue outbreaks in the last two decades in Kaohsiung follow a precise temporal pattern. Methods Government daily lab-confirmed dengue case data from three major dengue outbreaks occurring during the last two decades in Kaohsiung in 2002, 2014 and 2015, is utilized to compute the corresponding weekly cumulative percentage of total case numbers. We divide each of the three time series data into two periods to examine the corresponding weekly cumulative percentages of case numbers for each period. Pearson's correlation coefficient was calculated to compare quantitatively the similarity between the temporal patterns of these three years. Results Three cutoff points produce the most interesting comparisons and the most different outcomes. Pearson's correlation coefficient indicates quantitative discrepancies in the similarity between temporal patterns of the three years when using different cutoff points. Conclusions Temporal patterns in 2002 and 2014 are comparatively more similar in early stage. The 2015 outbreak started late in the year, but ended more like the outbreak in 2014, both with record-breaking number of cases. The retrospective analysis shows that the temporal dynamics of dengue outbreaks in Kaohsiung can strongly vary from one year to another, making it difficult to identify any common predictor. [ABSTRACT FROM AUTHOR]