A Pair Formation Model with Recovery: Application to Monkeypox
Bibliographic record
Abstract
The current global outbreaks of Monkeypox is a unique infectious disease in the way it seems to be transmitting: it has been observed to be highly concentrated in communities of men who have sex with men (MSM) through pair formation, and also provides immunity. This framework of mostly close, prolonged contact spreading a disease that admits immunity after infection is unlike similar infections which either offer little to no immunity post-infection or are lifelong infections. This creates the need for a new model framework that incorporates pair formation structure with recovery. While seemingly a straight forward model, we show how new dynamics arise from the combination of pair formation and recovery that are not present in a standard model with recovery and also not present in a pair formation model without recovery. We see that the combination of these two properties allows for waves of infection that are not seen in a standard SIR model. These dynamics suggest that outbreaks of monkeypox around the world may require special attention from public health. We also derive a reproduction number for this model and estimate the reproduction number of human monkeypox to be ≈ 2.3 using global and Canadian data. The expression derived for R 0 can help estimate key parameters for diseases transmission and public health interventions and compare to equivalent models without pair formation. Significance Statement With outbreaks of Monkeypox being observed around the world, a modeling framework which takes into account the unique properties of this emerging disease is necessary for understanding the disease and public health mitigation. Monkeypox seems to be unique in that it requires close, prolonged contact with an infected individual in order to spread, but also provides immunity after infection. We develop a model for this situation and show how this differs from simpler models which are currently being used for disease dynamics.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".