Seasonal Trends in Pediatric Respiratory Illnesses: Using Google Trends to Inform Precision Outreach.
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".