Reduced seasonal coronavirus incidence in high‐risk population groups during the COVID‐19 pandemic
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: 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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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 | 0.001 | 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 | 0.000 | 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