Google Flu Trends in Canada: a comparison of digital disease surveillance data with physician consultations and respiratory virus surveillance data, 2010–2014
Bibliographic record
Abstract
The value of Google Flu Trends (GFT) remains unclear after it overestimated the proportion of physician visits related to influenza-like illness (ILI) in the United States in 2012-2013. However, GFT estimates (%GFT) have not been examined nationally in Canada nor compared with positivity for respiratory viruses other than influenza. For 2010-2014, we compared %GFT for Canada to Public Health Agency of Canada ILI consultation rates (%PHAC) and to positivity for influenza A and B, respiratory syncytial virus (RSV), human metapneumovirus (hMPV), and rhinoviruses. %GFT correlated well with %PHAC (ρ = 0·77-0·90) and influenza A positivity (ρ = 0·64-0·96) and overestimated the 2012-2013 %PHAC peak by 0·99 percentage points. %GFT peaks corresponded temporally with peaks in positivity for influenza A and rhinoviruses (all seasons) and RSV and hMPV when their peaks preceded influenza peaks. In Canada, %GFT represented traditional surveillance data and corresponded temporally with patterns in circulating respiratory viruses.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".