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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.


Normalized weights in the multivariate time series model with “PL + pop.” weight.
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f3: Normalized weights in the multivariate time series model with “PL + pop.” weight.

Mentions: From the Table 1, the decay parameter estimate is  = 2.745 (0.672), which represents a strong decay of spatial interaction for higher-order neighbors because the higher the decay parameter d, the less important are higher-order neighbors. Moreover, Fig. 3 shows neighborhood weights wij against neighborhood order oij, it is obvious that the spatiotemporal component effects mainly account for nearest neighbors dependence.


Analysis of heterogeneous dengue transmission in Guangdong in 2014 with multivariate time series model
Normalized weights in the multivariate time series model with “PL + pop.” weight.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3: Normalized weights in the multivariate time series model with “PL + pop.” weight.
Mentions: From the Table 1, the decay parameter estimate is  = 2.745 (0.672), which represents a strong decay of spatial interaction for higher-order neighbors because the higher the decay parameter d, the less important are higher-order neighbors. Moreover, Fig. 3 shows neighborhood weights wij against neighborhood order oij, it is obvious that the spatiotemporal component effects mainly account for nearest neighbors dependence.

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.