Impact of COVID-19 on public social life and mental health: a statistical study of google trends data from the USA

Abstract

The COVID-19 pandemic has caused a significant disruption in the social lives and mental health of people across the world. This study aims to asses the effect of using internet search volume data. We categorize the widely searched keywords on the internet in several categories, which are relevant in analyzing the public mental health status. Corresponding to each category of keywords, we conduct an appropriate statistical analysis to identify significant changes in the search pattern during the course of the pandemic. Binary segmentation method of changepoint detection, along with the combination of ARMA-GARCH models are utilized in this analysis. It helps us detect how people's behavior changed in phases and whether the severity of the pandemic brought forth those shifts in behaviors. Interestingly, we find that rather than the severity of the outbreak, the long duration of the pandemic has affected the public health status more. The phases, however, align well with the so-called COVID-19 waves and are consistent for different aspects of social and mental health. We further observe that the results are typically similar for different states as well.

Keywords:

• ARMA-GARCH models
• changepoint detection
• coronavirus pandemic
• google search volume
• infodemiology

7. Data availability statement

COVID-19 data from the United States of America are used in this study. This dataset is publicly available in the GitHub repository maintained by Johns Hopkins CSSE (link: https://github.com/CSSEGISandData/COVID-19). Google Search Volume data is extracted from the Google Trends API using the pytrends library. All cleaned data and code used in this study can be made publicly available on request.

Disclosure statement

No potential conflict of interest was reported by the author(s).