Utility and limitations of Google searches on sensory loss as markers for new COVID-19 cases
Bibliographic record
Abstract
Abstract Evidence of smell loss in COVID-19 is growing. Researchers and analysts have suggested to use Google searches on smell loss as indicators of COVID-19 cases. However, such searches may be due to interest elicited by media coverage of the COVID-19-related smell loss, rather than attempts to understand self-symptoms. We analyzed searches related to 4 senses: smell and taste (both recently shown to be impaired in some COVID-19 patients), vision and sight (senses not currently known to be impaired in COVID-19 patients), and an additional general control (“COVID-19 symptoms”). Focusing on two countries with a large number of cases, Italy and the United States, we have compared Google Trends results per region or state to the number of new cases prevalence in that region. The analysis was performed for each of the 8 weeks ranging from March 4th till April 28th. No correlation with vision loss or sight loss searches was identified, while taste and smell loss searches were correlated with new COVID-19 cases during a limited time window, that starts when the number of weekly new cases reached for the first time 21357 cases in Italy (11-17 March) and 47553 in the US (18-24 March). Media effect on the specific symptoms searches was also analyzed, establishing a different impact according to the country. Our results suggest that Google Trends for taste loss and smell loss searches captured a genuine connection between these symptoms and new COVID-19 cases prevalence in the population. However, due to variability in correlation from week to week, and overall decrease in correlation as taste and smell loss are becoming known COVID-19 symptoms, recognized now by CDC and World Health Organization, Google Trends is no longer a reliable marker for monitoring the disease spread. The “surprise rise” followed by decrease, probably attributable to knowledge saturation, should be kept in mind for future digital media analyses of potential new symptoms of COVID-19 or future pandemics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.016 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".