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Analysis of heterogeneous dengue transmission in Guangdong in 2014 with multivariate time series model

View Article: PubMed Central - PubMed

ABSTRACT

Guangdong experienced the largest dengue epidemic in recent history. In 2014, the number of dengue cases was the highest in the previous 10 years and comprised more than 90% of all cases. In order to analyze heterogeneous transmission of dengue, a multivariate time series model decomposing dengue risk additively into endemic, autoregressive and spatiotemporal components was used to model dengue transmission. Moreover, random effects were introduced in the model to deal with heterogeneous dengue transmission and incidence levels and power law approach was embedded into the model to account for spatial interaction. There was little spatial variation in the autoregressive component. In contrast, for the endemic component, there was a pronounced heterogeneity between the Pearl River Delta area and the remaining districts. For the spatiotemporal component, there was considerable heterogeneity across districts with highest values in some western and eastern department. The results showed that the patterns driving dengue transmission were found by using clustering analysis. And endemic component contribution seems to be important in the Pearl River Delta area, where the incidence is high (95 per 100,000), while areas with relatively low incidence (4 per 100,000) are highly dependent on spatiotemporal spread and local autoregression.

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Three patterns and their characteristics.The autoregressive component trends are similar, and their percentages increase in all districts during the dengue outbreak period. In addition, red districts (Jiangmen, Zhongshan, Guangzhou and Foshan, Pattern A) have been mainly affected by the endemic component and are also the ones with the highest overall number of cases. Green districts (Maoming, Dongguan, Zhaoqing and Shantou, Pattern B) are mainly influenced by endemic and spatiotemporal components in the early period, and then the endemic proportion declines gradually, but the spatiotemporal proportion always maintains a relatively high level. The spatiotemporal component of incidence is the most powerful in blue districts (Chaozhou, Shaoguan, Huizhou, Jieyang, Heyuan, Shenzhen, Zhanjiang, Qingyuan and Yangjiang, Pattern C), meaning that a great number of cases are explained via transmission from neighboring districts (created with ArcGis Professional software version 10.2, http://www.esri.com/).
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f7: Three patterns and their characteristics.The autoregressive component trends are similar, and their percentages increase in all districts during the dengue outbreak period. In addition, red districts (Jiangmen, Zhongshan, Guangzhou and Foshan, Pattern A) have been mainly affected by the endemic component and are also the ones with the highest overall number of cases. Green districts (Maoming, Dongguan, Zhaoqing and Shantou, Pattern B) are mainly influenced by endemic and spatiotemporal components in the early period, and then the endemic proportion declines gradually, but the spatiotemporal proportion always maintains a relatively high level. The spatiotemporal component of incidence is the most powerful in blue districts (Chaozhou, Shaoguan, Huizhou, Jieyang, Heyuan, Shenzhen, Zhanjiang, Qingyuan and Yangjiang, Pattern C), meaning that a great number of cases are explained via transmission from neighboring districts (created with ArcGis Professional software version 10.2, http://www.esri.com/).

Mentions: It is obvious that three cluster centers and four noises can be found in the decision graph, as shown in Fig. 6. Thus, we hypothesize that there are three patterns driving dengue transmission in Guangdong, denoted as Patterns A, B, C, respectively (see Fig. 7, red districts belong to Pattern A, green districts belong to Pattern B and blue districts belong to Pattern C). It is worthwhile noting that autoregressive component trends in all districts are similar because there no significant differences among districts for the random effects in the autoregressive component as shown in Fig. 4(c). According to the Fig. 7. It is obvious that the autoregressive’s percentage increase during the dengue outbreak period, which means the local autoregression factor plays an increasingly important role in dengue progress in Guangdong. But the endemic and spatiotemporal components play different roles for different patterns.


Analysis of heterogeneous dengue transmission in Guangdong in 2014 with multivariate time series model
Three patterns and their characteristics.The autoregressive component trends are similar, and their percentages increase in all districts during the dengue outbreak period. In addition, red districts (Jiangmen, Zhongshan, Guangzhou and Foshan, Pattern A) have been mainly affected by the endemic component and are also the ones with the highest overall number of cases. Green districts (Maoming, Dongguan, Zhaoqing and Shantou, Pattern B) are mainly influenced by endemic and spatiotemporal components in the early period, and then the endemic proportion declines gradually, but the spatiotemporal proportion always maintains a relatively high level. The spatiotemporal component of incidence is the most powerful in blue districts (Chaozhou, Shaoguan, Huizhou, Jieyang, Heyuan, Shenzhen, Zhanjiang, Qingyuan and Yangjiang, Pattern C), meaning that a great number of cases are explained via transmission from neighboring districts (created with ArcGis Professional software version 10.2, http://www.esri.com/).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036033&req=5

f7: Three patterns and their characteristics.The autoregressive component trends are similar, and their percentages increase in all districts during the dengue outbreak period. In addition, red districts (Jiangmen, Zhongshan, Guangzhou and Foshan, Pattern A) have been mainly affected by the endemic component and are also the ones with the highest overall number of cases. Green districts (Maoming, Dongguan, Zhaoqing and Shantou, Pattern B) are mainly influenced by endemic and spatiotemporal components in the early period, and then the endemic proportion declines gradually, but the spatiotemporal proportion always maintains a relatively high level. The spatiotemporal component of incidence is the most powerful in blue districts (Chaozhou, Shaoguan, Huizhou, Jieyang, Heyuan, Shenzhen, Zhanjiang, Qingyuan and Yangjiang, Pattern C), meaning that a great number of cases are explained via transmission from neighboring districts (created with ArcGis Professional software version 10.2, http://www.esri.com/).
Mentions: It is obvious that three cluster centers and four noises can be found in the decision graph, as shown in Fig. 6. Thus, we hypothesize that there are three patterns driving dengue transmission in Guangdong, denoted as Patterns A, B, C, respectively (see Fig. 7, red districts belong to Pattern A, green districts belong to Pattern B and blue districts belong to Pattern C). It is worthwhile noting that autoregressive component trends in all districts are similar because there no significant differences among districts for the random effects in the autoregressive component as shown in Fig. 4(c). According to the Fig. 7. It is obvious that the autoregressive’s percentage increase during the dengue outbreak period, which means the local autoregression factor plays an increasingly important role in dengue progress in Guangdong. But the endemic and spatiotemporal components play different roles for different patterns.

View Article: PubMed Central - PubMed

ABSTRACT

Guangdong experienced the largest dengue epidemic in recent history. In 2014, the number of dengue cases was the highest in the previous 10 years and comprised more than 90% of all cases. In order to analyze heterogeneous transmission of dengue, a multivariate time series model decomposing dengue risk additively into endemic, autoregressive and spatiotemporal components was used to model dengue transmission. Moreover, random effects were introduced in the model to deal with heterogeneous dengue transmission and incidence levels and power law approach was embedded into the model to account for spatial interaction. There was little spatial variation in the autoregressive component. In contrast, for the endemic component, there was a pronounced heterogeneity between the Pearl River Delta area and the remaining districts. For the spatiotemporal component, there was considerable heterogeneity across districts with highest values in some western and eastern department. The results showed that the patterns driving dengue transmission were found by using clustering analysis. And endemic component contribution seems to be important in the Pearl River Delta area, where the incidence is high (95 per 100,000), while areas with relatively low incidence (4 per 100,000) are highly dependent on spatiotemporal spread and local autoregression.

No MeSH data available.


Related in: MedlinePlus