Trends of COVID-19 incidence in Manitoba and public health measures: March 2020 to February 2022
Bibliographic record
Abstract
Abstract Objectives The increasing spread of severe acute respiratory syndrome coronavirus-2 has prompted Canada to take unprecedented measures. The objective of this study was to examine the impact of the implemented public health measures on the incidence of COVID-19 in Manitoba. Results Using the COVID-19 dataset, we examined the temporal trends of daily reported COVID-19 cases and the coinciding public health measures implemented from March 12, 2020 to February 28, 2022. We calculated the 7-day moving average and crude COVID-19 infection rate/100,000 Manitobans. Due to the restrictions applied, the infection rate decreased from 2.4 (April 1) to 0.07 infections (May 1, 2020). Between May 4 and July 17, 2020, the reported cases stabilized, and some restrictions were lifted. However, in November, the cases peaked with infection rate of 29. Additional restrictions were implemented, and the rate dropped to 3.6 infections on March 31, 2021. As of August 2021, 62.8% of eligible Manitobans received two vaccine doses. The infection rate increased to 128.3 infections on December 31, 2021 and mitigation measures were implemented. This study describes how physical distancing in conjunction with other containment measures can reduce the COVID-19 burden. Future studies into the extent of the implementation of the restrictions are necessary.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 0.001 |
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".