Investigating the implications of COVID-19 for the rural and remote population of Northern Ontario using a mathematical model
Bibliographic record
Abstract
Abstract Background COVID-19 has the potential to disproportionately affect the rural, remote, and Indigenous populations who typically have a worse health status and live in substandard housing, often with overcrowding. Our aim is to investigate the potential effect of COVID-19 on intensive care unit (ICU) resources and mortality in northwestern Ontario. Methods This study was conducted in northwestern Ontario which has a population of 230,000. A set of differential equations were used to represent a modified Susceptible-Infectious-Recovered (SIR) model with urban and rural hospital resources (i.e., ICU and hospital beds). Rural patients requiring ICU care flowed into the urban ICU. Sensitivity analyses were used to investigate the effect of poorer health status (i.e., increased hospital admission, ICU admission, and mortality) and overcrowding (i.e., increased contact rate) in the rural population as compared to the urban population. Physical distancing within the urban population was modelled as a decreased contact rate. Results At the highest contact rate, the peak in daily active cases, ICU bed requirements and mortality was higher and occurred earlier than lower contact rates. The urban population with a lower contact rate and baseline health status had a lower predicted prevalence of active cases and lower mortality than the rural population. Interpretation An increased contact rate and worse health status in the rural population will likely increase the required ICU resources and mortality as compared to the urban population. Rural populations will likely be affected disproportionately more than urban populations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".