Predicting COVID-19 new cases in California with Google Trends data and a machine learning approach

ABSTRACT

Background

Google Trends data can be a valuable source of information for health-related issues such as predicting infectious disease trends.

Objectives

To evaluate the accuracy of predicting COVID-19 new cases in California using Google Trends data, we develop and use a GMDH-type neural network model and compare its performance with a LTSM model.

Methods

We predicted COVID-19 new cases using Google query data over three periods. Our first period covered March 1, 2020, to July 31, 2020, including the first peak of infection. We also estimated a model from October 1, 2020, to January 7, 2021, including the second wave of COVID-19 and avoiding possible biases from public interest in searching about the new pandemic. In addition, we extended our forecasting period from May 20, 2020, to January 31, 2021, to cover an extended period of time.

Results

Our findings show that Google relative search volume (RSV) can be used to accurately predict new COVID-19 cases.  We find that among our Google relative search volume terms, “Fever,” “COVID Testing,” “Signs of COVID,” “COVID Treatment,” and ”Shortness of Breath” increase model predictive accuracy.

Conclusions

Our findings highlight the value of using data sources providing near real-time data, e.g., Google Trends, to detect trends in COVID-19 cases, in order to supplement and extend existing epidemiological models.

KEYWORDS:

• COVID-19
• machine learning
• population health
• infodemiology
• text mining

Disclosure statement

The author(s) declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author contributions

A. Habibdoust M. Seifaddini, and M. Tatar: Conceptualization, Methodology, Software, Data curation. A. Habibdoust: Writing – Original draft preparation. A. Habibdoust and M. Seifaddini: Visualization, Investigation. Ozgur M. Araz and F. Wilson: Writing – Reviewing and Editing. F. Wilson: Supervision

Notes

¹ For more details about Methodology Framework for using Google Trends data, see reference number 35.

² Root mean square error.

³ Which remember the natural selection in evaluation theory.

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.