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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.

No MeSH data available.


Estimated district-specific random effects in the multivariate time series model.There is considerable variation concerning the endemic coefficient and spatiotemporal coefficient, and there seems to be little variation in the autoregressive coefficient (created with R version 3.3.3, https://www.r-project.org/).
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f4: Estimated district-specific random effects in the multivariate time series model.There is considerable variation concerning the endemic coefficient and spatiotemporal coefficient, and there seems to be little variation in the autoregressive coefficient (created with R version 3.3.3, https://www.r-project.org/).

Mentions: In particular, for the endemic component in Fig. 4(a), there is a pronounced heterogeneity between the Pearl River Delta area (Guangzhou, Foshan, Zhongshan, Jiangmen and Zhuhai, shown in pink) and the remaining districts. Similarly, there is considerable heterogeneity across districts for the spatiotemporal component as shown in Fig. 4(b). However, no clear spatial pattern can be seen. Moreover, all districts exhibit a very low random effect value in the autoregressive component according to the bar chart in Fig. 4(c). There seems to be no significant difference among districts for the random effects in the autoregressive component, thus we infer that the autoregressive effect accounting for dengue transmission in all districts might be homogeneous.


Analysis of heterogeneous dengue transmission in Guangdong in 2014 with multivariate time series model
Estimated district-specific random effects in the multivariate time series model.There is considerable variation concerning the endemic coefficient and spatiotemporal coefficient, and there seems to be little variation in the autoregressive coefficient (created with R version 3.3.3, https://www.r-project.org/).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Estimated district-specific random effects in the multivariate time series model.There is considerable variation concerning the endemic coefficient and spatiotemporal coefficient, and there seems to be little variation in the autoregressive coefficient (created with R version 3.3.3, https://www.r-project.org/).
Mentions: In particular, for the endemic component in Fig. 4(a), there is a pronounced heterogeneity between the Pearl River Delta area (Guangzhou, Foshan, Zhongshan, Jiangmen and Zhuhai, shown in pink) and the remaining districts. Similarly, there is considerable heterogeneity across districts for the spatiotemporal component as shown in Fig. 4(b). However, no clear spatial pattern can be seen. Moreover, all districts exhibit a very low random effect value in the autoregressive component according to the bar chart in Fig. 4(c). There seems to be no significant difference among districts for the random effects in the autoregressive component, thus we infer that the autoregressive effect accounting for dengue transmission in all districts might be homogeneous.

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.