Viral Interference and Coinfections: A Perspective From Hospital Surveillance of Respiratory Viruses
Bibliographic record
Abstract
Viral interference may influence pathogen transmission at the population level, potentially affecting seasonal epidemics of respiratory infections. A frequently employed association measure purported to reflect interference effects is the prevalence ratio, the proportion of individuals coinfected with two viruses divided by the product of the proportions of individuals infected by each virus separately. However, the prevalence ratio neglects three important factors relevant to coinfection detection in epidemiological surveillance programs: undetected mono-infections, duration of viral excretion or detectability and circulation patterns of both viruses. We propose a generalization of the prevalence ratio that accounts for these factors to better assess the presence or absence of viral interactions in epidemiological surveillance data. We applied this association measure to influenza-respiratory syncytial virus (RSV) coinfection data from a hospital-based surveillance program of respiratory infections in the province of Québec, Canada, from 2012-2013 to 2018-2019 (HospiVir program). Our analysis suggests that influenza-RSV interference may be important in children but less in adults. However, our results are sensitive to population-level seasonal attack rates; coinfection data could be compatible with interference in adults if assumed attack rates increased from 3% to 5% to over 10%.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".