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Developing a Time Series Predictive Model for Dengue in Zhongshan, China Based on Weather and Guangzhou Dengue Surveillance Data

Author

Listed:
  • Yingtao Zhang
  • Tao Wang
  • Kangkang Liu
  • Yao Xia
  • Yi Lu
  • Qinlong Jing
  • Zhicong Yang
  • Wenbiao Hu
  • Jiahai Lu

Abstract

Background: Dengue is a re-emerging infectious disease of humans, rapidly growing from endemic areas to dengue-free regions due to favorable conditions. In recent decades, Guangzhou has again suffered from several big outbreaks of dengue; as have its neighboring cities. This study aims to examine the impact of dengue epidemics in Guangzhou, China, and to develop a predictive model for Zhongshan based on local weather conditions and Guangzhou dengue surveillance information. Methods: We obtained weekly dengue case data from 1st January, 2005 to 31st December, 2014 for Guangzhou and Zhongshan city from the Chinese National Disease Surveillance Reporting System. Meteorological data was collected from the Zhongshan Weather Bureau and demographic data was collected from the Zhongshan Statistical Bureau. A negative binomial regression model with a log link function was used to analyze the relationship between weekly dengue cases in Guangzhou and Zhongshan, controlling for meteorological factors. Cross-correlation functions were applied to identify the time lags of the effect of each weather factor on weekly dengue cases. Models were validated using receiver operating characteristic (ROC) curves and k-fold cross-validation. Results: Our results showed that weekly dengue cases in Zhongshan were significantly associated with dengue cases in Guangzhou after the treatment of a 5 weeks prior moving average (Relative Risk (RR) = 2.016, 95% Confidence Interval (CI): 1.845–2.203), controlling for weather factors including minimum temperature, relative humidity, and rainfall. ROC curve analysis indicated our forecasting model performed well at different prediction thresholds, with 0.969 area under the receiver operating characteristic curve (AUC) for a threshold of 3 cases per week, 0.957 AUC for a threshold of 2 cases per week, and 0.938 AUC for a threshold of 1 case per week. Models established during k-fold cross-validation also had considerable AUC (average 0.938–0.967). The sensitivity and specificity obtained from k-fold cross-validation was 78.83% and 92.48% respectively, with a forecasting threshold of 3 cases per week; 91.17% and 91.39%, with a threshold of 2 cases; and 85.16% and 87.25% with a threshold of 1 case. The out-of-sample prediction for the epidemics in 2014 also showed satisfactory performance. Conclusion: Our study findings suggest that the occurrence of dengue outbreaks in Guangzhou could impact dengue outbreaks in Zhongshan under suitable weather conditions. Future studies should focus on developing integrated early warning systems for dengue transmission including local weather and human movement. Author Summary: Emerging and re-emerging infectious diseases in an urban city could expand due to increased urbanization, population density, and travel. Dengue, as a mosquito-borne viral disease, has rapidly spread from endemic areas to dengue-free regions, with social, demographic, entomological, and environmental factors affecting its transmission. In recent decades, Guangzhou has again suffered from several big outbreaks of dengue; as have its neighboring cities. In this study, we demonstrated that the dengue outbreaks in Guangzhou could impact outbreaks in Zhongshan, one of its neighboring cities, if suitable climate conditions are present. Such associations between dengue epidemics in two cities may also suggest the important role human movement has played in the transmission of the disease. Based on the association between dengue epidemics in Guangzhou and Zhongshan, and the association between dengue epidemics and weather conditions, we developed a reliable and robust model that predicts the occurrence of epidemics at diffrent thresholds in Zhongshan. These results could be used by local health departments in developing strategies towards dengue prevention and control, and push the public to pay more attention to social factors like human movement in disease transmission.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pntd00:0004473
    DOI: 10.1371/journal.pntd.0004473
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Yoonsuh Jung & Jianhua Hu, 2015. "A K -fold averaging cross-validation procedure," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(2), pages 167-179, June.
    4. Jiang Xu & Anthony G.O. Yeh, 2005. "City Repositioning and Competitiveness Building in Regional Development: New Development Strategies in Guangzhou, China," International Journal of Urban and Regional Research, Wiley Blackwell, vol. 29(2), pages 283-308, June.
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    1. Jundi Liu & Xiaolu Tian & Yu Deng & Zhicheng Du & Tianzhu Liang & Yuantao Hao & Dingmei Zhang, 2019. "Risk Factors Associated with Dengue Virus Infection in Guangdong Province: A Community-Based Case-Control Study," IJERPH, MDPI, vol. 16(4), pages 1-12, February.
    2. Sijia Wu & Hongyan Ren & Wenhui Chen & Tiegang Li, 2019. "Neglected Urban Villages in Current Vector Surveillance System: Evidences in Guangzhou, China," IJERPH, MDPI, vol. 17(1), pages 1-15, December.

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