When “Corona” Goes Viral – Online Search Interests in Google for Coronavirus versus Its Incidence in Australia, Canada, the United Kingdom, and the United States: An Infodemiological Analysis (Preprint)
Bibliographic record
Abstract
BACKGROUND The public health crisis, due to the new Coronavirus found in December 2019, has received unprecedented attention from the public and the media. The infodemiological analysis of queries from search engines to assess the status of search interests and the actual burden of the new virus could be an informative approach. OBJECTIVE The aim of the study was to assess search query data from Google Trends, to visualize the interest in search over time for the new “Coronavirus” in Google, across four English-speaking countries, namely, Australia, Canada, the UK, and the USA, and compare the search interest with the actual burden of Coronavirus in the corresponding countries. METHODS We used Google Trends service to assess people’s interest in searching about “Coronavirus” classified as “Virus,” from January 1, 2020 to March 13, 2020 in Australia, Canada, the UK, and the USA. Then, we evaluated top regions and their relative search volumes (SVs) and country-specific “Top” and “Rising” searches. We also evaluated the trends in the incidence of detected Coronavirus infections to find possible differences between the actual burden of the disease and search patterns by the public. RESULTS From January 1, 2020 to March 13, 2020, Australia was the top country searching for Coronavirus in Google, followed by Canada, the UK, and the USA. There was a noticeable bimodal pattern in searching for Coronavirus, mostly in late January 2020, and then from early March 2020. Search interest in all four countries declined in the month of February 2020. Top regions in each of the four countries with the highest search interest where the ones which reported either a confirmed case of Coronavirus infection or a death due to it. None of the declarations by the World Health Organization of the nature of this pandemic appeared to have caused major changes in the search patterns in Google. CONCLUSIONS Search for ‘Coronavirus’ increased exponentially, in all four countries, mostly in Australia. The month of February 2020 could be considered a ‘lost opportunity’ in terms of acting on the momentum of searching by people on Google about the Coronavirus. The increased interest in searching for keywords related to Coronavirus and its symptoms shows the possible focus areas of awareness campaigns in increasing societal demand for health information on the Web, to be met in community-wide communication or awareness interventions, should another pandemic occur in the future.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".