Characteristics of the sexual networks of gay, bisexual, and other men who have sex with men and impact of past interventions on mpox transmission during the 2022 outbreak in Montréal, Toronto, and Vancouver (Canada)
Bibliographic record
Abstract
Background: The 2022-2023 global mpox outbreak affected more than 90,000 people in 110 historically non-endemic countries, including Canada. Almost all reported Canadian cases were among gay, bisexual, and other men who have sex with men (GBM) and 70% of the cases occurred in the country’s three largest cities: Montréal, Toronto, and Vancouver. It remains unknown how characteristics of GBM sexual networks and public health interventions shaped mpox’s transmission dynamics in the three cities.Objectives: My thesis aims to understand mpox outbreaks and inform preparedness and response to re-emerging threats through data-driven statistical and mathematical modeling. Specifically, I addressed two main research questions: 1) How did GBM’s sexual networks affect mpox’s transmission potential in Montréal, Toronto, and Vancouver? 2) What is the relative contribution of changes in sexual partner numbers, contact tracing/isolation, and first-dose vaccination to the epidemic downturn of the 2022-2023 mpox outbreak?Methods: For 1), I leveraged the Engage Cohort Study (2017-present), which recruited self-identified GBM in Montréal, Toronto, or Vancouver via respondent-driven sampling (RDS). Using this data, I compared GBM’s self-reported number of sexual partners in the past 6 months (P6M) across cities and by time periods (i.e., pre-COVID-19 pandemic, pandemic, and after lifting travel restrictions). I modeled the distributions of sexual partners using Bayesian negative binomial regressions, adjusting for key correlates, survey weights, and loss to follow-up. I then developed a deterministic mathematical model to estimate mpox’s city-specific basic reproduction number (R0). For 2), I examined if GBM’s number of sexual partners changed during the peak of the mpox outbreak in Canada (May–August 2022) compared to the rest of 2022 using a negative binomial regression model. I expanded the deterministic mathematical model to estimate the averted fraction of new infections (AF) in the first 150 days of the outbreak through the three interventions separately, as compared to the counterfactual scenario of an unmitigated epidemics.Results: A total of 2,449 GBM participated in Engage (Montréal: 1,179; Toronto: 517; Vancouver: 753). The pre-COVID-19 pandemic distribution of sexual partner numbers (P6M) was similar across cities: participants’ mean number of partners was 10.4 (95% credible interval [CrI]: 9.4-11.5) in Montréal, 13.1 (11.3-15.1) in Toronto, and 10.7 (9.5-12.1) in Vancouver. Partner numbers decreased greatly during the COVID-19 pandemic in all cities: 4.7 (4.0-5.5) in Montréal, 4.3 (3.3-5.8) in Toronto, and 5.5 (4.3-7.3) in Vancouver. Post-travel-restrictions, sexual partner numbers increased but remained well below pre-pandemic levels: 5.5 (4.7-6.4) in Montréal, 7.2 (5.7-9.1) in Toronto, and 6.7 (5.3-8.4) in Vancouver. The estimated R0 for mpox varied from 2.4 to 2.7 between cities. During the peak of the mpox outbreak, GBM might have had fewer sexual partners compared to the rest of 2022, but the estimates were imprecise. A larger decline was observed among GBM with >7 sexual partners (P6M) before 2022 (rate ratio [RR]: 0.67, 95%CrI: 0.31-1.43), as compared to 0.80 (0.47-1.36) among those with ≤ 7 sexual partner, but credible intervals were overlapping and very wide. Cases prevented by changes in sexual partner numbers and contact tracing/isolation were around 12% and 14% in the cities, respectively. Vaccination averted most cases in all cities, contributing to 21% (16%-33%), 22% (16%-41%), and 39% (35%-48%) of infections prevented in Montréal, Toronto, and Vancouver, respectively.Conclusions: The 2022-23 mpox outbreak in Canada occurred while sexual activity had not yet recovered to pre-pandemic levels and ongoing surveillance is warranted. In case of mpox resurgence, ensuring contact tracing/isolation, as well as increasing vaccination coverage among individuals with high numbers of sexual partners, should be prioritized
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".