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. 2015 May 28;9(5):e0003808.
doi: 10.1371/journal.pntd.0003808. eCollection 2015 May.

Predicting unprecedented dengue outbreak using imported cases and climatic factors in Guangzhou, 2014

Shaowei Sang  1 , Shaohua Gu  2 , Peng Bi  3 , Weizhong Yang  4 , Zhicong Yang  5 , Lei Xu  2 , Jun Yang  2 , Xiaobo Liu  2 , Tong Jiang  6 , Haixia Wu  2 , Cordia Chu  7 , Qiyong Liu  8
Affiliations

Predicting unprecedented dengue outbreak using imported cases and climatic factors in Guangzhou, 2014

Shaowei Sang et al. PLoS Negl Trop Dis. .

Abstract

Introduction: Dengue is endemic in more than 100 countries, mainly in tropical and subtropical regions, and the incidence has increased 30-fold in the past 50 years. The situation of dengue in China has become more and more severe, with an unprecedented dengue outbreak hitting south China in 2014. Building a dengue early warning system is therefore urgent and necessary for timely and effective response.

Methodology and principal findings: In the study we developed a time series Poisson multivariate regression model using imported dengue cases, local minimum temperature and accumulative precipitation to predict the dengue occurrence in four districts of Guangzhou, China. The time series data were decomposed into seasonal, trend and remainder components using a seasonal-trend decomposition procedure based on loess (STL). The time lag of climatic factors included in the model was chosen based on Spearman correlation analysis. Autocorrelation, seasonality and long-term trend were controlled in the model. A best model was selected and validated using Generalized Cross Validation (GCV) score and residual test. The data from March 2006 to December 2012 were used to develop the model while the data from January 2013 to September 2014 were employed to validate the model. Time series Poisson model showed that imported cases in the previous month, minimum temperature in the previous month and accumulative precipitation with three month lags could project the dengue outbreaks occurred in 2013 and 2014 after controlling the autocorrelation, seasonality and long-term trend.

Conclusions: Together with the sole transmission vector Aedes albopictus, imported cases, monthly minimum temperature and monthly accumulative precipitation may be used to develop a low-cost effective early warning system.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The study areas in Guangzhou city, China.
Fig 2
Fig 2. The decomposition plot of the time series in the study areas from March 2006 to September 2014.
A) The decomposition plot of local dengue cases with logarithm transformation; B) The decomposition plot of imported cases; C) The decomposition plot of monthly minimum temperature; D) The decomposition plot of monthly accumulative precipitation. The top layer shows the original time series. The other layers show the decomposed components, denoting the seasonal component, long term trend component and remainder component, respectively.
Fig 3
Fig 3. The estimated effects of local cases in the previous month with logarithm transformation (A), imported cases in the previous month (B), minimum temperature in the previous month (C) and cumulative precipitation with three month lags with logarithm transformation (D) on local dengue incidence.
Fig 4
Fig 4. Monthly reported local dengue cases and fitted dengue cases from March 2006 to December 2012.
Fig 5
Fig 5. Forecasted dengue cases versus reported local dengue cases from January 2013 to September 2014.
The dashed lines represented 95% credible intervals of forecasted dengue cases.
See this image and copyright information in PMC

References

    1. Guzman MG, Harris E (2014) Dengue. Lancet: 1–13. 10.1016/S2214-109X(15)70024-0 - DOI - PubMed
    1. WHO (2015) Global Alert and Response. Impact of Dengue. [Cited 20 January 2015]. Avaliable: http://www.who.int/csr/disease/ dengue/impact/en/.
    1. Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, et al. (2013) The global distribution and burden of dengue. Nature 496: 504–507. 10.1038/nature12060 - DOI - PMC - PubMed
    1. WHO (2012) Global strategy for dengue prevention and control. Geneva: World Health Organization.
    1. Stoddard ST, Morrison AC, Vazquez-Prokopec GM, Paz Soldan V, Kochel TJ, et al. (2009) The role of human movement in the transmission of vector-borne pathogens. PLoS Negl Trop Dis 3: e481 10.1371/journal.pntd.0000481 - DOI - PMC - PubMed

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