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Dynamic Forecasting of Zika Epidemics Using Google Trends

Author

Listed:
  • Yue Teng
  • Dehua Bi
  • Guigang Xie
  • Yuan Jin
  • Yong Huang
  • Baihan Lin
  • Xiaoping An
  • Dan Feng
  • Yigang Tong

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

Suggested Citation

  • Yue Teng & Dehua Bi & Guigang Xie & Yuan Jin & Yong Huang & Baihan Lin & Xiaoping An & Dan Feng & Yigang Tong, 2017. "Dynamic Forecasting of Zika Epidemics Using Google Trends," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-10, January.
    • Handle: RePEc:plo:pone00:0165085
    DOI: 10.1371/journal.pone.0165085
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    Cited by:

    1. Grzegorz Michal Bulczak, 2021. "Use of Google Trends to Predict the Real Estate Market: Evidence from the United Kingdom," International Real Estate Review, Global Social Science Institute, vol. 24(4), pages 613-631.
    2. Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 33-54.
    3. Marwah Soliman & Vyacheslav Lyubchich & Yulia R. Gel, 2020. "Ensemble forecasting of the Zika space‐time spread with topological data analysis," Environmetrics, John Wiley & Sons, Ltd., vol. 31(7), November.
    4. D.V. Firsov & T.C. Chernyshevа, 2021. "Review of Successful Practices of Applying Nowcasting in Socio-Economic Forecasting," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 20(2), pages 269-293.
    5. Jiam Song & Kwangmin Jung & Jonghun Kam, 2023. "Evidence of the time-varying impacts of the COVID-19 pandemic on online search activities relating to shopping products in South Korea," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    6. Mohammad Reza Farzanegan & Mehdi Feizi & Saeed Malek Sadati, 2020. "Google It Up! A Google Trends-based analysis of COVID-19 outbreak in Iran," MAGKS Papers on Economics 202017, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    7. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    8. Vaishnav, Vaibhav & Vajpai, Jayashri, 2020. "Assessment of impact of relaxation in lockdown and forecast of preparation for combating COVID-19 pandemic in India using Group Method of Data Handling," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    9. Prasanth, Sikakollu & Singh, Uttam & Kumar, Arun & Tikkiwal, Vinay Anand & Chong, Peter H.J., 2021. "Forecasting spread of COVID-19 using google trends: A hybrid GWO-deep learning approach," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    10. Pei-Ying Kobres & Jean-Paul Chretien & Michael A Johansson & Jeffrey J Morgan & Pai-Yei Whung & Harshini Mukundan & Sara Y Del Valle & Brett M Forshey & Talia M Quandelacy & Matthew Biggerstaff & Ceci, 2019. "A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 13(10), pages 1-21, October.

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