Bibliographic record
Abstract
Objectives Google Trends is an emerging tool that allows users to analyze search queries, showing when certain topics are searched most often. Multiple studies have compared Google Trends to epidemiological data of health conditions, but pediatric specific illnesses have not yet been investigated. An association between disease incidence and Google Trends data may help facilitate precision outreach in the form of digital resources and promotion. We sought to examine the relationship between Google Trends data and measured incidence of bronchiolitis and croup. Methods We carried out a Google Trends search using the terms “bronchiolitis” and “croup” on July 24, 2019. The number of positive respiratory syncytial virus and parainfluenza tests published by the Public Health Agency of Canada was used to estimate incidence of bronchiolitis and croup, respectively. Emergency department discharge data were used to measure the number of patients with bronchiolitis and croup presenting to a Canadian pediatric hospital. Data from January 1, 2015, to December 31, 2018, were used for analysis. Results Google Trends revealed clear seasonal variation in search volume for both bronchiolitis and croup in keeping with known epidemiological data for these conditions. For data on bronchiolitis, Google Trends correlated strongly with Canadian Public Health and our hospital data. A positive correlation was also found with croup. Conclusions Google Trends correlates with both laboratory-based and hospital incidence of respiratory viral diagnoses. This novel data source has implications for tracking disease epidemiology, tailoring health information, and providing precision outreach tools to patients and their families.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".