SPATIAL MODELLING OF COVID-19 INCIDENCE RATE IN CANADA
Bibliographic record
Abstract
Abstract. The COVID-19 was first declared by World Health Organization (WHO) as global pandemic on March 11th 2020. While most of COVID-related studies have focused on epidemiological perspective, the spatial analysis of disease outbreak is also important to provide perceptions of transmission rates. Therefore, this paper attempts to identify the potential factors contributing to the COVID-19 incidence rate at provincial-level in Canada. Three statistical regression models, ordinary least squares (OLS), spatial error model, and spatial lag model (SLM) were applied to 14 independent variables including socio-demographic, economic, weather, health and facilities related factors. The results indicated that three factors including median income, diabetes and unemployment significantly affected the COVID-19 rates in Canada. Among three global models, the SLM performed the best to explain the key variables and spatial variability of disease incidence with a R2 value of 61%. However, in this study, the application of local regression models such as geographically weighted regression (GWR) and multiscale GWR (MGWR) have not been considered and this could be a scope for the future research.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".