Complexity and the intersection of social and sexual structure, ecological niches and the epidemic potential of sexually transmitted and bloodborne infections: empirical and theoretical observations
Bibliographic record
Abstract
Introduction: Incomplete understanding of how context explains heterogeneity in transmission dynamics of sexually transmitted and bloodborne infections (STBBIs) has led to deficiencies in prevention and control activities. Like place-based analyses, social network analysis has held much promise for incorporating context to the study of STBBIs. The costs and complexity associated with empirical network data have limited its full potential. Recent advances in the use of exponential random graph models (ERGM) and molecular epidemiology have re-invigorated network-based research. ERGM theory focuses on local processes creating global network structure, embodying a generative approach to network formation; this approach contends that networks unfold and evolve predictably, thus epidemics should also be similarly predictable. This dissertation aims to combine traditional surveillance methods with advances in network methodologies and orient their use to an applied public health context. Methods: Using public health surveillance data, and focusing on the epidemiology of STBBIs in Winnipeg, the three studies employed a context-based perspective in understanding underlying processes creating observed empirical data. The inequality in the distribution of STBBIs was examined. Networks created through molecular genotyping and through traditional case-and-contact investigations were compared using descriptive statistics and univariate network metrics. Stochastic simulation modelling, based on the ERGM framework, examined the interaction between pathogen characteristics, mixing patterns and network topology. Results: Each STBBI had its own ecological niche, although these were malleable over time. Geographic inequality in the distribution of gonorrhea was decreasing in the context of a growth phase, while also occupying similar geographic space as chlamydia. Molecular epidemiology served a complementary role, revealing potentially hidden links between cases. The most successfully transmitted gonorrhea subtype was associated with chlamydia co-infection. Simulation modelling revealed a relationship between assortative mixing and pathogen infection duration; high levels of assortative mixing muted the modeled epidemic trajectory, with the most drastic effect on infections with shorter duration of infectivity. Conclusion: The three studies cohesively address current challenges in applying context to public health analyses, while expanding our understanding of the mechanisms needed to alter the trajectory of STBBI epidemics. Insights gained from the included analyses form the basis of a proposed context-based surveillance framework.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".