ArticleScientific reports2016
Analysis of heterogeneous dengue transmission in Guangdong in 2014 with multivariate time series model.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Transmission components of hand, foot, and mouth disease in Guangdong Province and their association with meteorological factors: a modelling study.BMC infectious diseases · 2025Article
- Spatiotemporal dynamics and potential ecological drivers of acute respiratory infectious diseases: an example of scarlet fever in Sichuan Province.BMC public health · 2022Article
- A spatio-temporal autoregressive model for monitoring and predicting COVID infection rates.Journal of geographical systems · 2022Article
- Estimating the annual dengue force of infection from the age of reporting primary infections across urban centres in endemic countries.BMC medicine · 2021Article
- Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model.International journal of environmental research and public health · 2021Article
- Modelling and predicting the spatio-temporal spread of cOVID-19 in Italy.BMC infectious diseases · 2020Article
- Spatio-temporal dynamics of measles outbreaks in Cameroon.Annals of epidemiology · 2020Article
- Assessing the effect of containment measures on the spatio-temporal dynamic of COVID-19 in Italy.Nonlinear dynamics · 2020Article
- Spatiotemporal analysis of the dengue outbreak in Guangdong Province, China.BMC infectious diseases · 2019Article
- Spatial-temporal characteristics and the epidemiology of haemorrhagic fever with renal syndrome from 2007 to 2016 in Zhejiang Province, China.Scientific reports · 2018Article
- Human movement, cooperation and the effectiveness of coordinated vector control strategies.Journal of the Royal Society, Interface · 2017Article
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Authors and funding
5 authors.
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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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