Reduced seasonal coronavirus incidence in high‐risk population groups during the COVID‐19 pandemic
Bibliographic record
Abstract
BACKGROUND: Epidemiological data on seasonal coronaviruses (sCoVs) may provide insight on transmission patterns and demographic factors that favor coronaviruses (CoVs) with greater disease severity. This study describes the incidence of CoVs in several high-risk groups in Ottawa, Canada, from October 2020 to March 2022. METHODS: Serological assays quantified IgG and IgM antibodies to SARS-CoV-2, HCoV-OC43, HCoV-NL63, HCoV-HKU1, and HCoV-229E. Incident infections were compared between four population groups: individuals exposed to children, transit users, immunocompromised, and controls. Associations between antibody prevalence indicative of natural infection and demographic variables were assessed using regression analyses. RESULTS: Transit users and those exposed to children were at no greater risk of infection compared to the control group. Fewer infections were detected in the immunocompromised group (p = .03). SARS-CoV-2 seroprevalence was greater in individuals with low income and within ethnic minorities. CONCLUSIONS: Our findings suggest that nonpharmaceutical interventions intended to reduce SAR-CoV-2 transmission protected populations at high risk of exposure. The re-emergence of sCoVs and other common respiratory viruses alongside SARS-CoV-2 may alter infection patterns and increase the risk in vulnerable populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".