Public interest trends for Covid-19 and alignment with the disease trajectory: A time-series analysis of national-level data

Data from web search engines have become a valuable adjunct in epidemiology and public health, specifically during epidemics. We aimed to explore the concordance of web search popularity for Covid-19 across 6 Western nations (United Kingdom, United States, France, Italy, Spain and Germany) and how timeline changes align with the pandemic waves, Covid-19 mortality, and incident case trajectories. We used the Google Trends tool for web-search popularity, and "Our World in Data" on Covid-19 reported cases, deaths, and administrative responses (measured by stringency index) to analyze country-level data. The Google Trends tool provides spatiotemporal data, scaled to a range of <1 (lowest relative popularity) to 100 (highest relative popularity), for the selected search terms, timeframe, and region. We used "coronavirus" and "covid" as search terms and set the timeframe up to November 12, 2022. We obtained multiple consecutive samples using the same terms to validate against sampling bias. We consolidated national-level incident cases and deaths weekly and transformed them to a range between 0 to 100 through the min-max normalization algorithm. We calculated the concordance of relative popularity rankings between regions, using the non-parametric Kendall's W, which maps concordance between 0 (lack of agreement) to 1 (perfect match). We used a dynamic time-warping algorithm to explore the similarity between Covid-19 relative popularity, mortality, and incident case trajectories. This methodology can recognize the similarity of shapes between time-series through a distance optimization process. The peak popularity was recorded on March 2020, to be followed by a decline below 20% in the subsequent three months and a long-standing period of variation around that level. At the end of 2021, public interest spiked shortly to fade away to a low level of around 10%. This pattern was highly concordant across the six regions (Kendal's W 0.88, p< .001). In dynamic time warping analysis, national-level public interest yielded a high similarity with the Covid-19 mortality trajectory (Similarity indices range 0.60-0.79). Instead, public interest was less similar with incident cases (0.50-0.76) and stringency index trajectories (0.33-0.64). We demonstrated that public interest is better intertwined with population mortality, rather than incident case trajectory and administrative responses. As the public interest in Covid-19 gradually subsides, these observations could help predict future public interest in pandemic events.