Google search data could predict new COVID-19 cases

In a recent study posted to Research Square*, researchers showed that Google search data could be utilized for monitoring the spread of coronavirus 2019 (COVID-19) cases.

Study: The Evolution of the COVID-19 Pandemic Through the Lens of Google Searches. Image Credit: Olga Rolenko/Shutterstock
Study: The Evolution of the COVID-19 Pandemic Through the Lens of Google Searches. Image Credit: Olga Rolenko/Shutterstock

Background

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has changed human lives. For more than two years, people adopted various measures like face masks, testing, and social distancing; dealt with academic or business closures and unemployment; and learned to accept vaccines made with new technologies, i.e., messenger ribonucleic acid (mRNA)-based vaccines.

People relied on Google to search about COVID-19 symptoms, policies, and vaccines, which generates an enormous amount of data about people’s fears, symptoms, concerns, and demand for information and could be exceptionally valuable to policymakers.

About the study

In the present study, researchers investigated if the Google search data could be used to anticipate the rise in COVID-19 cases. The team evaluated the socioeconomic impact of containment policies and examined the demand for information regarding vaccines and misinformation.

Search interest data from 207 countries and territories were queried from September 2018 to December 2021. Sixty-eight search terms about COVID-19 symptoms, socioeconomic consequences, mental health, and vaccines were queried. A correlation coefficient was computed between search interest and COVID-19 cases for each country using a seven-day moving average of cases and search interests. The impact of containment policies (business or school closures, movement restrictions, social distancing, etc.) on search interest was evaluated.

The team assessed how the impact of containment policies varied with the restrictiveness of the policies, income levels, and the level of economic support from governments. Vaccination data on a daily basis was retrieved from Our World in Data. Search interests were queried for terms across general vaccination terms, safety and side effects of vaccines, appointments, and misinformation. Finally, a case study of the United States (US) at a subnational level was conducted to analyze vaccination and search interests.

Results

The authors observed that search interest for COVID-19 symptoms strongly correlated with and preceding COVID-19 cases. In 2021, the median correlation was high but lower than in 2022 for search interest in terms like loss of smell or taste, indicating their relevance and usefulness even late in the pandemic. Search terms like ‘coronavirus” and ‘how to treat coronavirus’ were negatively correlated with COVID-19 cases in 2020 across many countries but positively correlated in 2021.

The researchers believed that global news drove search interests in 2020, whereas personal experience about COVID-19 and country-specific information drove 2021 search interests. Fever and pneumonia had lower mean and median correlation values than COVID-19-specific terms. A negative optimal lag was found across many countries, implying that search interest trends preceded the trends in recorded/observed cases. Interest in searching for loss of smell was less predictive of cases with the SARS-CoV-2 Omicron variant, while interests in COVID-19 symptoms were predictive.

Containment measures increased the search interests for phrases like mental health, unemployment, and physical distancing while search interests for family and relationship planning terms decreased. In countries where economic support from the government was greater, higher searchers were recorded for unemployment and debt-related terms and lower search interests for suicide and anxiety.

Moreover, search interests for words like lonely, boredom, social isolation, and panic were significantly higher. In countries with more restrictive measures, search interests for keywords related to unemployment and mental health were higher, and lower interests were noted for wedding and divorce. In lower-income countries, search interests were lower for unemployment, panic, social distancing, or boredom-related terms but higher for insomnia, suicide, anxiety attacks, and family or relationship planning terms.

Higher-income countries exhibited a higher correlation between vaccination rate and interest in searching for vaccine appointments or reactions and vaccines. Search interests for misinformation-related terms were less correlated with the vaccination rate. Across the US, search interests for words like COVID-19 vaccine, vaccine appointment, safety, and side effects of COVID-19 were strongly correlated with vaccination rates.

In contrast, interest in Ivermectin was negatively correlated with the vaccination rate. Search interests for ivermectin, a popular misinformation term, were higher in states with lower vaccination rates. At the same time, other misinformation terms like COVID-19 infertility, vaccine microchip, and vaccine mercury, among others, were equally popular across states with high and low vaccination rates.

Conclusions

The present study demonstrated that search data from Google could be helpful in monitoring and understanding the COVID-19 pandemic. Throughout the pandemic, search interests in symptoms specific to COVID-19 were strongly correlated with COVID-19 cases with a lag of 12 days in 2020 and two to six days in 2021, implying that search trends tend to precede observed COVID-19 cases.

These (correlation) results were equally predictive of new COVID-19 cases in high- and low-income countries. To conclude, Google search data could help monitor the spread and consequences of the COVID-19 pandemic, albeit with a few limitations, including that global rather than country-specific dynamics could influence search interests, which might be an imperfect proxy for the situation on the ground.

*Important notice

Research Square publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Journal reference:
Tarun Sai Lomte

Written by

Tarun Sai Lomte

Tarun is a writer based in Hyderabad, India. He has a Master’s degree in Biotechnology from the University of Hyderabad and is enthusiastic about scientific research. He enjoys reading research papers and literature reviews and is passionate about writing.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Sai Lomte, Tarun. (2022, April 27). Google search data could predict new COVID-19 cases. News-Medical. Retrieved on July 06, 2022 from https://www.news-medical.net/news/20220427/Google-search-data-could-predict-new-COVID-19-cases.aspx.

  • MLA

    Sai Lomte, Tarun. "Google search data could predict new COVID-19 cases". News-Medical. 06 July 2022. <https://www.news-medical.net/news/20220427/Google-search-data-could-predict-new-COVID-19-cases.aspx>.

  • Chicago

    Sai Lomte, Tarun. "Google search data could predict new COVID-19 cases". News-Medical. https://www.news-medical.net/news/20220427/Google-search-data-could-predict-new-COVID-19-cases.aspx. (accessed July 06, 2022).

  • Harvard

    Sai Lomte, Tarun. 2022. Google search data could predict new COVID-19 cases. News-Medical, viewed 06 July 2022, https://www.news-medical.net/news/20220427/Google-search-data-could-predict-new-COVID-19-cases.aspx.

Comments

The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of News Medical.
Post a new comment
Post
You might also like...
The severity of Omicron BA.4/BA.5 lineages in the South Africa population