Effects of Various Policy Options on COVID-19 Cases in Nova Scotia including Vaccination Rollout Schedule: A Modelling Study
Bibliographic record
Abstract
ABSTRACT Background The COVID-19 pandemic presents a significant challenge to minimize mortality and hospitalizations due to this disease. Vaccinations have begun to roll-out; however, restriction policies required during and after the rollout remain uncertain. A susceptible-exposed-infected-recovered (SEIR) model was developed for Nova Scotia, and it accounted for the province’s policy interventions, demographics, and vaccine rollout schedule. Methods A modified SEIR model was developed to simulate the spread and outcomes from COVID-19 in Nova Scotia under different policy options. The model incorporated the age distribution and co-morbidity of the province. A system dynamics model was developed in Vensim. Several scenarios were run to determine the effects of various policy options and loosening of restrictions during and after the vaccine roll-out period. Results When restrictions policy include moderate closure of businesses, restricting travel to Atlantic Canada, and the mandating of masks and physical distancing, the number of cumulative infections after 110 days was less than 120. However, if national travel was opened by July 5 2021 and there were no restrictions by September 2021, the number of active infections will peak at 6,114 by February 16 2022, and there will be a peak of 104 hospitalizations on February 16 2022. Immediate opening of travel and all restrictions on March 15, 2021 will result in 71,731 active infections by June 4 2021. Discussion Moderate restrictions will be required even after the population is fully vaccinated in order to avoid a large number of infections and hospitalizations because herd immunity is not reached due to children under 12 not being vaccinated, the efficacy of the vaccine, and the portion of the population that will choose not to be vaccinated.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".