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Dynamic forecasting of Zika epidemics using Google Trends

Yue Teng, Dehua Bi, Guigang Xie, Guigang Xie, Yuan Jin, Baihan Lin, Dan Feng
doi: https://doi.org/10.1101/076521
Yue Teng
1Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China;
2State Key Laboratory of Pathogen and Biosecurity, Beijing 100071, China;
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  • For correspondence: yueteng@sklpb.org
Dehua Bi
2State Key Laboratory of Pathogen and Biosecurity, Beijing 100071, China;
3Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada;
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Guigang Xie
2State Key Laboratory of Pathogen and Biosecurity, Beijing 100071, China;
4State Key Laboratory for Conservation and Utilization of Subtropical AgroYbioresources, The Key Laboratory of Ministry of Education for Microbial and Plant Genetic Engineering, and College of Life Science and Technology, Guangxi University, 100 Daxue Road, Nanning 530004, China;
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Guigang Xie
2State Key Laboratory of Pathogen and Biosecurity, Beijing 100071, China;
4State Key Laboratory for Conservation and Utilization of Subtropical AgroYbioresources, The Key Laboratory of Ministry of Education for Microbial and Plant Genetic Engineering, and College of Life Science and Technology, Guangxi University, 100 Daxue Road, Nanning 530004, China;
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Yuan Jin
2State Key Laboratory of Pathogen and Biosecurity, Beijing 100071, China;
5Beijing Institute of Biotechnology, Beijing 100071, China;
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Baihan Lin
6Computational Neuroscience Program, Department of Psychology, Physics, and Computer Science and Engineering; Institute for Protein Design, University of Washington, Seattle, WA 98195, USA;
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Dan Feng
7Division of Standard Operational Management, Institute of Hospital Management, Chinese PLA General Hospital, Beijing 100853, China;
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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 for Health Departments with early warning, and predictions of numbers of infection cases, which would allow them sufficient time to implement interventions. We used correlation data from ZIKV epidemics and Zika-related online search in GTs between 12 February and 25 August 2016 to construct an autoregressive integrated moving average (ARIMA) model (0, 1, 3) for the dynamic estimation of ZIKV outbreaks. The online search data acted as an external regressor in the forecasting model, and was used with the historical ZVD epidemic data to improve the quality of the predictions of disease outbreaks. Our results showed a strong correlation between Zika-related GTs and the cumulative numbers of reported cases, both confirmed and suspected (both p<0.001; Pearson Product-Moment Correlation analysis). The predictive cumulative numbers of confirmed and suspected cases increased steadily to reach 148,510 (95% CI: 126,826-170,195) and 602,721 (95% CI: 582,753-622,689), respectively, in 21 October 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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted September 22, 2016.
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Dynamic forecasting of Zika epidemics using Google Trends
Yue Teng, Dehua Bi, Guigang Xie, Guigang Xie, Yuan Jin, Baihan Lin, Dan Feng
bioRxiv 076521; doi: https://doi.org/10.1101/076521
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Dynamic forecasting of Zika epidemics using Google Trends
Yue Teng, Dehua Bi, Guigang Xie, Guigang Xie, Yuan Jin, Baihan Lin, Dan Feng
bioRxiv 076521; doi: https://doi.org/10.1101/076521

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