Changes in the Incidence of Invasive Pneumococcal Disease in Calgary, Canada, during the SARS-CoV-2 Pandemic 2020–2022
Bibliographic record
Abstract
We describe the impact of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic on invasive pneumococcal disease (IPD) in Calgary. IPD declined significantly worldwide during 2020 and 2021. This may be due to the reduced transmission of and decrease in circulating viruses that often co-infect with the opportunistic pneumococcus. Pneumococcus has not been shown to frequently co-infect or cause secondary infection with SARS-CoV-2. We examined and compared incidence rates in Calgary per quarter in the pre-vaccine, post-vaccine, 2020 and 2021 (pandemic) and 2022 (late pandemic) eras. We also conducted a time series analysis from 2000-2022 allowing for change in trend at introduction of vaccines and for initiation of NPIs during the COVID-19 pandemic. Incidence declined in 2020/2021 but by the end of 2022 had begun to rapidly recover to near pre-vaccine rates. This recovery may be related to the high rates of viral activity in the winter of 2022 along with childhood vaccines being delayed during the pandemic. However, a large proportion of the IPD caused in the last quarter of 2022 was serotype 4, which has caused outbreaks in the homeless population of Calgary in the past. Further surveillance will be important to understand IPD incidence trends in the post-pandemic landscape.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".