Leveraging Population-Based Modelling Approaches to Inform Respiratory Disease Prevention
Bibliographic record
Abstract
Severe acute respiratory syndrome 2 (SARS-CoV-2) and pneumococcal disease are vaccine-preventable but remain leading causes of morbidity and mortality in young children and older adults. In this dissertation, I present three population-based studies which inform respiratory disease prevention using public health surveillance data. In a population-based cohort study, I linked reported SARS-CoV-2 cases with vaccination records in Ontario, Canada. I found that vaccination is associated with lower odds of hospitalization among adolescent and pediatric Omicron (B.1.1.529) SARS-CoV-2 cases, even when the vaccines do not prevent infection. I developed and analyzed a dynamic pneumococcal transmission model fit to age-specific invasive pneumococcal disease (IPD) incidence in Canada. Using the fitted model, I found that the use of 13-valent pneumococcal conjugate vaccines in pediatric populations prevented 1,275 IPD cases across the population, with the majority of cases averted in older adults. In a self-matched case-crossover study, I estimated the impact of acute changes in influenza A, influenza B, and respiratory syncytial virus (RSV) activity on IPD risk. I found that influenza A activity and influenza B activity are independently associated with increased IPD risk. However, the co-circulation of influenza A and B reduced the impact of both viruses. RSV activity was positively associated with increased IPD risk only in the presence of increased influenza activity. Overall, these results contribute to our understanding of vaccine-preventable respiratory diseases in Canada. These results can inform strategies to prevent morbidity and mortality from respiratory diseases.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".