title: Developing a Time Series Predictive Model for Dengue in Zhongshan, China Based on Weather and Guangzhou Dengue Surveillance Data.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong Province, P. R. China.
authors.forename: Yingtao
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Zhongshan Center for Disease Control and Prevention, Zhongshan, Guangdong Province, P. R. China.|Zhongshan Institute of School of Public Health, Sun Yat-sen University, Zhongshan, Guangdong Province, P. R. China.
authors.forename: Tao
authors.initials: T
authors.lastname: Wang
authors.affiliation: Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong Province, P. R. China.
authors.forename: Kangkang
authors.initials: K
authors.lastname: Liu
authors.affiliation: Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong Province, P. R. China.
authors.forename: Yao
authors.initials: Y
authors.lastname: Xia
authors.affiliation: Department of Environmental Health, School of Public Health, University at Albany, State University of New York, Albany, New York, United States of America.
authors.forename: Yi
authors.initials: Y
authors.lastname: Lu
authors.affiliation: Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong Province, P. R. China.|Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong Province, P. R. China.
authors.forename: Qinlong
authors.initials: Q
authors.lastname: Jing
authors.affiliation: Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong Province, P. R. China.
authors.forename: Zhicong
authors.initials: Z
authors.lastname: Yang
authors.affiliation: School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.|Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia.
authors.forename: Wenbiao
authors.initials: W
authors.lastname: Hu
authors.affiliation: Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong Province, P. R. China.|Zhongshan Institute of School of Public Health, Sun Yat-sen University, Zhongshan, Guangdong Province, P. R. China.|Key Laboratory for Tropical Diseases Control of Ministry of Education, Sun Yat-sen University, Guangzhou, Guangdong Province, P. R. China.|One Health Center of Excellence for Research and Training, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong Province, P. R. China.|Institute of Emergency Technology for Serious Infectious Diseases Control and Prevention, Guangdong Provincial Department of Science and Technology; Emergency Management Office, the People's Government of Guangdong Province, Guangzhou, P. R. China.|Center of Inspection and Quarantine, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong Province, P. R. China.
authors.forename: Jiahai
authors.initials: J
authors.lastname: Lu
mesh_terms: D002681:China / Q000453:epidemiology; D003715:Dengue / Q000453:epidemiology* / Q000635:transmission; D004196:Disease Outbreaks; D005260:Female; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis; D018571:Sentinel Surveillance; D014887:Weather