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Predicting Local Dengue Transmission in Guangzhou, China, through the Influence of Imported Cases, Mosquito Density and Climate Variability

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  • Shaowei Sang
  • Wenwu Yin
  • Peng Bi
  • Honglong Zhang
  • Chenggang Wang
  • Xiaobo Liu
  • Bin Chen
  • Weizhong Yang
  • Qiyong Liu

Abstract

Introduction: Each year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF), a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explore the roles of imported cases, mosquito density and climate variability in dengue transmission in China. The study was to identify the relationship between dengue occurrence and possible risk factors and to develop a predicting model for dengue’s control and prevention purpose. Methodology and Principal Findings: Three traditional suburbs and one district with an international airport in Guangzhou city were selected as the study areas. Autocorrelation and cross-correlation analysis were used to perform univariate analysis to identify possible risk factors, with relevant lagged effects, associated with local dengue cases. Principal component analysis (PCA) was applied to extract principal components and PCA score was used to represent the original variables to reduce multi-collinearity. Combining the univariate analysis and prior knowledge, time-series Poisson regression analysis was conducted to quantify the relationship between weather variables, Breteau Index, imported DF cases and the local dengue transmission in Guangzhou, China. The goodness-of-fit of the constructed model was determined by pseudo-R2, Akaike information criterion (AIC) and residual test. There were a total of 707 notified local DF cases from March 2006 to December 2012, with a seasonal distribution from August to November. There were a total of 65 notified imported DF cases from 20 countries, with forty-six cases (70.8%) imported from Southeast Asia. The model showed that local DF cases were positively associated with mosquito density, imported cases, temperature, precipitation, vapour pressure and minimum relative humidity, whilst being negatively associated with air pressure, with different time lags. Conclusions: Imported DF cases and mosquito density play a critical role in local DF transmission, together with weather variables. The establishment of an early warning system, using existing surveillance datasets will help to control and prevent dengue in Guangzhou, China.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0102755
    DOI: 10.1371/journal.pone.0102755
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    References listed on IDEAS

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    1. Miranda Chan & Michael A Johansson, 2012. "The Incubation Periods of Dengue Viruses," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-7, November.
    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. Elodie Descloux & Morgan Mangeas & Christophe Eugène Menkes & Matthieu Lengaigne & Anne Leroy & Temaui Tehei & Laurent Guillaumot & Magali Teurlai & Ann-Claire Gourinat & Justus Benzler & Anne Pfannst, 2012. "Climate-Based Models for Understanding and Forecasting Dengue Epidemics," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 6(2), pages 1-19, February.
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    3. Michael Xiaoliang Tong & Alana Hansen & Scott Hanson-Easey & Scott Cameron & Jianjun Xiang & Qiyong Liu & Yehuan Sun & Philip Weinstein & Gil-Soo Han & Craig Williams & Peng Bi, 2015. "Infectious Diseases, Urbanization and Climate Change: Challenges in Future China," IJERPH, MDPI, vol. 12(9), pages 1-12, September.
    4. Yingtao Zhang & Tao Wang & Kangkang Liu & Yao Xia & Yi Lu & Qinlong Jing & Zhicong Yang & Wenbiao Hu & Jiahai Lu, 2016. "Developing a Time Series Predictive Model for Dengue in Zhongshan, China Based on Weather and Guangzhou Dengue Surveillance Data," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 10(2), pages 1-17, February.
    5. Hongyan Ren & Lan Zheng & Qiaoxuan Li & Wu Yuan & Liang Lu, 2017. "Exploring Determinants of Spatial Variations in the Dengue Fever Epidemic Using Geographically Weighted Regression Model: A Case Study in the Joint Guangzhou-Foshan Area, China, 2014," IJERPH, MDPI, vol. 14(12), pages 1-13, December.
    6. 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.
    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. 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.

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