A Spatial Epidemiology Case Study of Coronavirus (COVID-19) Disease and Geospatial Technologies
Bibliographic record
Abstract
Spatiotemporal pattern analysis provides a new dimension for data interpretation due to new trends in computer vision and big data analysis. The main aim of this study was to explore the recent advances in geospatial technologies to examine the spatiotemporal pattern of COVID-19 at the Public Health Unit (PHU) level in Ontario, Canada. The spatial autocorrelation results showed that the incidence rate (no. of confirmed cases per 100,000 population–IR/100K) was clustered at the PHU level and found a tendency of clustering high values. Some PHUs in Southern Ontario were identified as hot spots, while Northern PHUs were cold spots. The space-time cube showed an overall trend with a 99% confidence level. Considerable spatial variability in incidence intensity at different times suggested that risk factors were unevenly distributed in space and time. The study also created a regression model that explains the correlation between IR/100K values and potential socioeconomic factors.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".