Using Mathematical Models to Inform Syphilis Control Strategies in Men Who Have Sex With Men
Bibliographic record
Abstract
Syphilis is resurgent in many high-income countries, disproportionately affecting urban men who have sex with men (MSM). Frequent screening of at-risk individuals remains the best available tool for syphilis control, but current public health efforts are not resulting in reduced disease burden. The aim of this thesis was to use mathematical modeling to understand the effect of different approaches to syphilis screening on epidemic dynamics and the health of MSM. An agent-based model of syphilis transmission in a core group of sexually active MSM was parameterized with data on the epidemiology of the current epidemic to evaluate plausible screening strategies that might be employed for epidemic control. Of the strategies evaluated, more frequent screening of at-risk MSM already accessing screening, rather than expanding outreach to provide screening to unscreened individuals, was found to be the most effective means of reducing syphilis incidence over a 10-year intervention period. A state-transition microsimulation model of syphilis natural history and medical care was developed to determine the cost-effectiveness of incorporating routine syphilis testing into the blood-work of MSM under care for HIV. When rates of syphilis acquisition were high, opt-out syphilis screening in HIV-infected MSM was projected to be a highly cost-effective intervention. A risk-structured deterministic compartmental mathematical model of syphilis transmission in MSM was used to examine the impact of sustained syphilis screening at varying levels of population coverage. Increasing screening in a population with initially low levels of coverage was shown to lead to increases in infection incidence. Although screening has the potential to control syphilis outbreaks, suboptimal screening coverage may result in the establishment of higher equilibrium infection incidence than that observed in the absence of the intervention, possibly contributing to outbreak persistence. The results of this research suggest that current control efforts are not expected to reduce syphilis incidence, with more effective screening programs required to reduce syphilis burden in MSM. The work presented in this thesis provides some insight into factors that may lead to screening programs that both improve the health of individuals and reduce the overall population burden, ultimately resulting in improved epidemic control.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".