Spatial epidemiological analysis of Lyme disease in southern Ontario utilizing Google Trends searches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lyme disease is of growing concern in Ontario with endemic areas increasing in size. Differential diagnosis of Lyme disease patients should include their exposure status assuming knowledge of high-risk areas. The goal of this study was a spatial analysis of Lyme disease in southern Ontario for the years 2015–2019 with a focus on the association between Lyme disease prevalence and Internet search frequencies recorded by Google Trends. A choropleth map visualized the raw prevalence of Lyme disease across the 28 public health units of southern Ontario. A disease cluster comprising five public health units was identified in eastern Ontario using the flexible scan statistic (standard morbidity ratio = 4.9, p = 0.01). Poisson regression modeling revealed an association between Lyme disease prevalence and the search term “Lyme disease” in Google Trends (p = 0.032). Lyme disease prevalence was correlated with Google Trend searches, with an increase in relative risk by a factor of 1.19 (CI 95% : 1.03, 1.39) for every 1% increase in search activity. Knowledge of the existence and location of high-risk or exposure areas for Lyme disease is important to properly diagnose patients. Exploiting the association between Lyme disease and Internet search activity by the population at risk can also further disease surveillance.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.015 | 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 it