Riding high: seroprevalence of SARS-CoV-2 after 4 pandemic waves in Manitoba, Canada, April 2020–February 2022
Bibliographic record
Abstract
BACKGROUND: Canada is emerging from the largest SARS-CoV-2 Omicron wave to date, with over 3.3 million confirmed cases. Unfortunately, PCR confirmed cases illuminate only a small portion of infections in the community and underestimate true disease burden. Population based seroprevalence studies, which measure antibody levels against a virus can more accurately estimate infection rates in the community and identify geographical and epidemiological trends to inform public health responses. METHODS: The Manitoba COVID-19 Seroprevalence (MCS) study is a population-based cross-sectional study to assess the prevalence of SARS-CoV-2 antibodies across the province. Residual convenience specimens (n = 14,901) were tested for anti-SARS-CoV-2 nucleocapsid and spike IgG antibodies from April 1, 2020 to February 31, 2022. We estimated the monthly and cumulative prevalence using an exponential decay model, accounting for population demographics, sensitivity/specificity, and antibody waning. This approach generated estimates of natural infection as well as total antibody including vaccine-induced immunity within the community. FINDINGS: After four waves of the pandemic, 60.1% (95%CI-56.6-63.7) of Manitobans have generated SARS-CoV-2 antibodies due to natural exposure independent of vaccination. Geographical analysis indicates a large portion of provincial prevalence stems from increased transmission in the Northern (92.3%) and Southern (71.8%) regional health authorities. Despite the high mortality rates reported by Manitoba, infection fatality ratios (IFR) peaked at 0.67% and declined to 0.20% following the Omicron wave, indicating parity with other national and international jurisdictions. Manitoba has achieved 93.4% (95%CI- 91.5-95.1) total antibody when including vaccination. INTERPRETATION: Our data shows that more than 3 in 5 Manitobans have been infected by SARS-CoV-2 after four waves of the pandemic. This study also identifies key geographical and age specific prevalence rates that have contributed greatly to the overall severity of the pandemic in Manitoba and will inform jurisdictions considering reduction of public health measures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".