A Retrospective Study of the Effects of COVID-19 Non-Pharmaceutical Interventions on Influenza in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background/Objectives: COVID-19 pandemic had a significant impact on endemic respiratory illnesses. Through behavioral changes in populations and government policy, mainly through non-pharmaceutical interventions (NPIs), Canada saw historic lows in the number of influenza A cases from 2020 through 2022. In this study, we use historical influenza A data for Canada and three provincial jurisdictions within Canada—Ontario, Quebec, and Alberta—to quantify the effects of these NPIs on influenza A. Methods: We aim to see which base parameters and derived parameters of an SIR model are most affected by NPIs. We fit a simple SIR model to historical influenza data to get average paramters for seasonal influenza. We then compare these parameters to those predicted by fitting influenza cases during the COVID-19 pandemic. Results: We find substantial differences in the effective population size and basic reproduction number during the COVID-19 pandemic. We also see the effects of fatigue and relaxation of NPIs when comparing the years 2020, 2021, and 2022. Conclusions: We find that the effective population size is the main driver of change to disease spread and discuss how these retrospective estimates can be used for future forecasting.
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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 | 0.000 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it