Google searches may aid in COVID-19 response
Looking at internet search patterns could help authorities stay one step ahead of the novel coronavirus disease (COVID-19), according to a recent study. In the future, internet surveillance and keyword trends may be leveraged for infectious disease control.
“Our study found moderate to strong correlations between data obtained from searching COVID-19-related keywords in Google Trends and total COVID-19 cases in the United States as obtained from national data aggregators,” the researchers said. “Strong correlations were seen up to 16 days prior to the first reported cases in some states”
Ten keyword strings were fed into Google Trends to identify search patterns across different states. The resulting trends were compared against COVID-19 case data, obtained from the Johns Hopkins University Center for Systems Science and Engineering. The analytic period ran from 22 January to 6 April 2020.
In general, all keywords analysed saw increasing popularity over the study period, though some, such as “COVID symptoms” and “loss of smell” saw peaks by mid-to-late March. Others, such as “face mask” and “antibody” saw continuing and increasing interest until the study’s end. [Mayo Clin Proc 2020;doi:10.1016/j.mayocp.2020.08.022]
When looking at the US, “antibody” shared the strongest correlation with the daily new COVID-19 cases, with a Pearson correlation coefficient value of 0.88. On the other hand, the keyword “coronavirus symptoms” showed the least interaction (R, 0.06).
Three keywords (“antibody,” “face mask,” and “COVID-19 stimulus check”) had coefficient values within the range of 0.7–1.0, suggesting that these strongly correlated with daily cases. Six other keywords, including “loss of smell” and “coronavirus testing center,” were moderately associated with daily cases.
The popularity of each keyword varied greatly according to state and was influenced by the number of COVID-19 cases. For instance, searches for “antibody” and “Lysol” grew as the number of COVID-19 cases increased. On the other hand, interest for terms such as “COVID symptoms” and “coronavirus vaccine” was higher when the cases were lower.
To better understand the interaction between keywords and case counts, the researchers calculated for the lag and lead Pearson correlation coefficients for all states and for the entire country as a whole. They found that most keywords were most strongly searched for days before the first COVID-19 case was reported, and interest diminished afterward.
For instance, “coronavirus symptoms” was most popular 16 days before the first case but saw a steep decline thereafter. “Loss of smell,” in comparison, had a spike in search approximately 2 weeks before the first case. On the other hand, other terms like “antibody” and “face mask” peaked around the time the first COVID-19 case was documented.
“This study reveals the benefits of internet surveillance models and the use of Google Trends to monitor new infectious diseases such as COVID-19. For the United States, Google Trends data were highly correlated with cases of COVID-19 on a state-by-state basis and could potentially be used to predict new areas of outbreak and possible high-impact zones as the disease progresses,” the researchers said.
“Furthermore, this study documents that there is information present in Google Trends that precedes outbreaks, and these data should be utilized to allow for better resource allocation in regard to tests, personal protective equipment, medication, and more,” they added.