The association between COVID-19 cases and deaths and web-based public inquiries
Bibliographic record
Abstract
Corona Virus Disease 2019 (COVID-19) emerged in December 2019 and rapidly spread globally. Since there is still no specific treatment available, prevention of disease spread is crucial to manage the pandemic. Adequate public information is very important. To assess the optimal timing, the aim of this study was to investigate the association between web-based interest and new cases and deaths due to COVID-19. Web-based interest for queries related to ‘coronavirus’ was assessed between 1 January and 19 June 2020, using Google Trends in Australia, Brazil, Canada, Germany, Italy, South Africa, South Korea, Spain, United Kingdom, and the United States of America. Reliability analysis of the used search terms was performed using the intraclass correlation coefficient. To investigate the association between web-based interest and new COVID-19 cases or deaths, the relative search volume was analysed for correlation with new cases and deaths. Reliability analysis revealed excellent reliability for COVID-19 search terms in all countries. Web-based interest peaked between 23 February and 5 April 2020, which was prior to the peak of new infections and deaths in most included countries. There was a moderate to strong correlation between COVID-19 related queries and new cases or new deaths. Web-based interest in COVID-19 peaked prior to the peak of new infections and deaths in most countries included. Thus, monitoring public interest via Google Trends might be useful to select the optimal-timing of web-based disease-specific information and preventive measures.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".