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Predicting Unprecedented Dengue Outbreak Using Imported Cases and Climatic Factors in Guangzhou, 2014

Author

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  • Shaowei Sang
  • Shaohua Gu
  • Peng Bi
  • Weizhong Yang
  • Zhicong Yang
  • Lei Xu
  • Jun Yang
  • Xiaobo Liu
  • Tong Jiang
  • Haixia Wu
  • Cordia Chu
  • Qiyong Liu

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. Author Summary: Dengue has emerged as the most important mosquito-borne viral disease globally. With increasing global trade and population movement, the disease is transferred to regions which were previously dengue free. When dengue vector exists and weather factors are suitable, there is the possibility for dengue transmission and even outbreaks happening. Dengue is still believed to be a non-endemic disease in China, with imported cases playing a vital role in local dengue transmission. The situation of dengue is becoming more and more severe with two successive large outbreaks hitting southern China in 2013 and 2014, and the dengue outbreak in 2014 was unprecedented. In this study, we aim to develop a dengue forecasting model that would provide an early warning of dengue outbreak to allow local health authorities and communities to implement timely effective control measures. Our model showed that imported cases in the previous month, monthly minimum temperature in the previous month and monthly accumulative precipitation with three month lags could predict dengue outbreak ahead by one month. We concluded that these variables could be used to develop an early dengue warning model to provide evidence-based decisions for disease control and prevention and including the utilization of limited resources.

Suggested Citation

  • Shaowei Sang & Shaohua Gu & Peng Bi & Weizhong Yang & Zhicong Yang & Lei Xu & Jun Yang & Xiaobo Liu & Tong Jiang & Haixia Wu & Cordia Chu & Qiyong Liu, 2015. "Predicting Unprecedented Dengue Outbreak Using Imported Cases and Climatic Factors in Guangzhou, 2014," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 9(5), pages 1-12, May.
    • Handle: RePEc:plo:pntd00:0003808
    DOI: 10.1371/journal.pntd.0003808
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    1. Samir Bhatt & Peter W. Gething & Oliver J. Brady & Jane P. Messina & Andrew W. Farlow & Catherine L. Moyes & John M. Drake & John S. Brownstein & Anne G. Hoen & Osman Sankoh & Monica F. Myers & Dylan , 2013. "The global distribution and burden of dengue," Nature, Nature, vol. 496(7446), pages 504-507, April.
    2. Yien Ling Hii & Huaiping Zhu & Nawi Ng & Lee Ching Ng & Joacim Rocklöv, 2012. "Forecast of Dengue Incidence Using Temperature and Rainfall," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 6(11), pages 1-9, November.
    3. Yien Ling Hii & Joacim Rocklöv & Stig Wall & Lee Ching Ng & Choon Siang Tang & Nawi Ng, 2012. "Optimal Lead Time for Dengue Forecast," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 6(10), pages 1-9, October.
    4. Shaowei Sang & Wenwu Yin & Peng Bi & Honglong Zhang & Chenggang Wang & Xiaobo Liu & Bin Chen & Weizhong Yang & Qiyong Liu, 2014. "Predicting Local Dengue Transmission in Guangzhou, China, through the Influence of Imported Cases, Mosquito Density and Climate Variability," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-10, July.
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    2. Yujuan Yue & Xiaobo Liu & Dongsheng Ren & Haixia Wu & Qiyong Liu, 2021. "Spatial Dynamics of Dengue Fever in Mainland China, 2019," IJERPH, MDPI, vol. 18(6), pages 1-12, March.
    3. Dan Liu & Songjing Guo & Mingjun Zou & Cong Chen & Fei Deng & Zhong Xie & Sheng Hu & Liang Wu, 2019. "A dengue fever predicting model based on Baidu search index data and climate data in South China," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-16, December.
    4. Elorm Donkor & Matthew Kelly & Cecilia Eliason & Charles Amotoh & Darren J. Gray & Archie C. A. Clements & Kinley Wangdi, 2021. "A Bayesian Spatio-Temporal Analysis of Malaria in the Greater Accra Region of Ghana from 2015 to 2019," IJERPH, MDPI, vol. 18(11), pages 1-15, June.
    5. Laith Hussain-Alkhateeb & Tatiana Rivera Ramírez & Axel Kroeger & Ernesto Gozzer & Silvia Runge-Ranzinger, 2021. "Early warning systems (EWSs) for chikungunya, dengue, malaria, yellow fever, and Zika outbreaks: What is the evidence? A scoping review," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 15(9), pages 1-25, September.
    6. Yujuan Yue & Qiyong Liu & Xiaobo Liu & Ning Zhao & Wenwu Yin, 2022. "Dengue Fever in Mainland China, 2005–2020: A Descriptive Analysis of Dengue Cases and Aedes Data," IJERPH, MDPI, vol. 19(7), pages 1-13, March.
    7. Yujuan Yue & Qiyong Liu, 2019. "Exploring Epidemiological Characteristics of Domestic Imported Dengue Fever in Mainland China, 2014–2018," IJERPH, MDPI, vol. 16(20), pages 1-10, October.
    8. Kinley Wangdi & Kinley Penjor & Tobgyal & Saranath Lawpoolsri & Ric N. Price & Peter W. Gething & Darren J. Gray & Elivelton Da Silva Fonseca & Archie C. A. Clements, 2021. "Space–Time Clustering Characteristics of Malaria in Bhutan at the End Stages of Elimination," IJERPH, MDPI, vol. 18(11), pages 1-12, May.

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