Abstract
Google Trends provides spatiotemporal data for user-specific terms scaled from less than 1 (lowest relative popularity) to 100 (highest relative popularity) as a proxy for the public interest. Here we use US state-level data for COVID-19 to examine popularity trends during the pandemic evolution. We used "coronavirus" and "covid" search terms and set the period up from January 1st, 2020, to November 12, 2022. We measured the agreement on web rankings between states using the nonparametric Kendall’s W (0 for no concordance to 1 for perfect agreement). We compiled state-level weekly data on COVID-19 incidence and mortality and scaled state curves from 0 to 100 through a min-max normalization process. We used a dynamic time-warping algorithm to calculate similarities between the popularity, mortality, and incidence of COVID-19. The methodology is a pattern recognition process between time series by distance optimization. The similarity was mapped from 0 to 1, with 1 indicating perfect similarity and 0 indicating no similarity. The peak in popularity was in March 2020, succeeded by a decline and a prolonged period of fluctuation around 20%. Public interest rose briefly at the end of 2021, to fall to a low activity of around 10%. This pattern was remarkably consistent across states (Kendal’s W 0.94, p < 0.001). Web search trends were an impression of contagion growth: Overall, popularity-mortality trajectories yielded higher similarity indices (median 0.78; interquartile range 0.75–0.82) compared to popularity-incidence trajectories (median 0.74; interquartile range 0.72–0.76, Wilcoxon’s exact p<0.001). The popularity-mortality trajectories had a very strong similarity (>0.80) in 19/51 (37%) regions, as opposed to only 4/51 (8%) for popularity-incidence trajectories. State-level data show a fading public concern about COVID-19, and web-search popularity patterns may reflect the COVID-19 trajectory in terms of cases and mortality.
Publisher
Public Library of Science (PLoS)