title: Dynamic Forecasting of Zika Epidemics Using Google Trends.
abstract: We developed a dynamic forecasting model for Zika virus (ZIKV), based on real-time online search data from Google Trends (GTs). It was designed to provide Zika virus disease (ZVD) surveillance and detection for Health Departments, and predictive numbers of infection cases, which would allow them sufficient time to implement interventions. In this study, we found a strong correlation between Zika-related GTs and the cumulative numbers of reported cases (confirmed, suspected and total cases; p<0.001). Then, we used the correlation data from Zika-related online search in GTs and ZIKV epidemics between 12 February and 20 October 2016 to construct an autoregressive integrated moving average (ARIMA) model (0, 1, 3) for the dynamic estimation of ZIKV outbreaks. The forecasting results indicated that the predicted data by ARIMA model, which used the online search data as the external regressor to enhance the forecasting model and assist the historical epidemic data in improving the quality of the predictions, are quite similar to the actual data during ZIKV epidemic early November 2016. Integer-valued autoregression provides a useful base predictive model for ZVD cases. This is enhanced by the incorporation of GTs data, confirming the prognostic utility of search query based surveillance. This accessible and flexible dynamic forecast model could be used in the monitoring of ZVD to provide advanced warning of future ZIKV outbreaks.
authors.affiliation: Beijing Institute of Microbiology and Epidemiology, Beijing, China.|State Key Laboratory of Pathogen and Biosecurity, Beijing, China.
authors.forename: Yue
authors.initials: Y
authors.lastname: Teng
authors.affiliation: State Key Laboratory of Pathogen and Biosecurity, Beijing, China.|Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Ontario, Canada.
authors.forename: Dehua
authors.initials: D
authors.lastname: Bi
authors.affiliation: State Key Laboratory of Pathogen and Biosecurity, Beijing, China.
authors.forename: Guigang
authors.initials: G
authors.lastname: Xie
authors.affiliation: State Key Laboratory of Pathogen and Biosecurity, Beijing, China.|Beijing Institute of Biotechnology, Beijing, China.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Jin
authors.affiliation: Beijing Institute of Microbiology and Epidemiology, Beijing, China.|State Key Laboratory of Pathogen and Biosecurity, Beijing, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Huang
authors.affiliation: Computational Neuroscience Program, Department of Psychology, Physics, and Computer Science and Engineering; Institute for Protein Design, University of Washington, Seattle, United States of America.
authors.forename: Baihan
authors.initials: B
authors.lastname: Lin
authors.affiliation: Beijing Institute of Microbiology and Epidemiology, Beijing, China.|State Key Laboratory of Pathogen and Biosecurity, Beijing, China.
authors.forename: Xiaoping
authors.initials: X
authors.lastname: An
authors.affiliation: Division of Standard Operational Management, Institute of Hospital Management, Chinese PLA General Hospital, Beijing, China.
authors.forename: Dan
authors.initials: D
authors.lastname: Feng
authors.affiliation: Beijing Institute of Microbiology and Epidemiology, Beijing, China.|State Key Laboratory of Pathogen and Biosecurity, Beijing, China.
authors.forename: Yigang
authors.initials: Y
authors.lastname: Tong
mesh_terms: D004196:Disease Outbreaks; D005544:Forecasting / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D015233:Models, Statistical; D061108:Social Media; D000071244:Zika Virus; D000071243:Zika Virus Infection / Q000453:epidemiology* / Q000821:virology