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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.


Fitted components in the multivariate time series model for 20 districts with more than 0 cases.Black dots are drawn for weekly counts, the light gray area shows the estimated endemic component, the blue area corresponds to the autoregressive contribution and the orange area corresponds to the spatiotemporal contribution.
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f5: Fitted components in the multivariate time series model for 20 districts with more than 0 cases.Black dots are drawn for weekly counts, the light gray area shows the estimated endemic component, the blue area corresponds to the autoregressive contribution and the orange area corresponds to the spatiotemporal contribution.

Mentions: For each district, the relative contributions of endemic, autoregressive and spatiotemporal factors in driving the dengue prevalence with time is called the “patterns driving dengue transmission” in this district. An intuitive way of quantifying the relative contributions of the three components is provided by Fig. 5. It shows the fitted component means along with the observed time series for the 20 districts with at least one case. Figure 5 also demonstrates that dengue transmission appears to be synchronous in Guangdong, peaking at the same times of the year in different districts (between weeks 35 and 44). In order to further understand different aspects of dengue transmission patterns and their drivers, we focus on the outbreak period (weeks 35 to 44) and use the Fdp clustering method (see Method Section) to find patterns driving dengue transmission.


Analysis of heterogeneous dengue transmission in Guangdong in 2014 with multivariate time series model
Fitted components in the multivariate time series model for 20 districts with more than 0 cases.Black dots are drawn for weekly counts, the light gray area shows the estimated endemic component, the blue area corresponds to the autoregressive contribution and the orange area corresponds to the spatiotemporal contribution.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f5: Fitted components in the multivariate time series model for 20 districts with more than 0 cases.Black dots are drawn for weekly counts, the light gray area shows the estimated endemic component, the blue area corresponds to the autoregressive contribution and the orange area corresponds to the spatiotemporal contribution.
Mentions: For each district, the relative contributions of endemic, autoregressive and spatiotemporal factors in driving the dengue prevalence with time is called the “patterns driving dengue transmission” in this district. An intuitive way of quantifying the relative contributions of the three components is provided by Fig. 5. It shows the fitted component means along with the observed time series for the 20 districts with at least one case. Figure 5 also demonstrates that dengue transmission appears to be synchronous in Guangdong, peaking at the same times of the year in different districts (between weeks 35 and 44). In order to further understand different aspects of dengue transmission patterns and their drivers, we focus on the outbreak period (weeks 35 to 44) and use the Fdp clustering method (see Method Section) to find patterns driving dengue transmission.

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.