Modelling vaccination and control strategies of outbreaks of monkeypox at gatherings
Bibliographic record
Abstract
Abstract Background Monkeypox cases keep soaring in non-endemic’s countries and areas in the last few months, leading to the WHO declaring a Public Health Emergency of International Concern. The ongoing and coming festivals, parties and holidays gathering events are causing increased concerns about possible outbreaks. Methods We considered a hypothetical metropolitan city and modelled the transmission of monkeypox virus in humans in high-risk (HRG) and low-risk groups (LRG) using a Susceptible-Exposed-Infectious-Recovered (SEIR) model and incorporated gathering events. Model simulations assessed how the current vaccination strategy combined with other public health measures can contribute to mitigating or halting outbreaks from mass gathering events. Results The risk of a monkeypox outbreak remains high on the occasion of mass gathering events in the absence of public health control measures. However, the outbreaks can be well controlled by cutting off transmission by isolating confirmed cases and inoculating their close contacts. Also, Post Exposure Prophylaxis is more effective for containment in the summer gatherings than a broad vaccination campaign in HRG, considering the time needed for developing the immune response and the availability of vaccine. The number of attendees and effective contacts during the gathering are the factors that need more attention by public health authorities to prevent a burgeoning outbreak. Moreover, restricting attendance through vaccination requirements can help secure mass gathering events. Conclusion Gathering events can be made safe with some restrictions of either the number and density of attendees in the gathering, or vaccination requirements. The ring vaccination strategy inoculating close contacts of confirmed cases may not be enough to prevent potential outbreaks, however, mass gatherings can be rendered safe if that strategy is combined with public health measures, including rigorous contact tracing, testing, identifying and isolating cases. Compliance of the community and promotion of awareness are also indispensable to the containment.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 3 models reading the full record.
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".