Mathematical modelling and time series clustering of Mpox outbreak: A comparative study of the top 10 affected countries and implications for future outbreak management
Bibliographic record
Abstract
The 2022 Mpox outbreak, characterized by its rapid cross-continental spread beyond traditionally endemic regions, presented a renewed threat to global health security. This study presents a comparative epidemiological analysis of the ten countries most affected by Mpox, integrating mathematical modelling with time series clustering, the first of its kind to analyze the 2022 WHO Mpox data. By applying an SIR-based model to estimate the effective transmission rate, basic reproduction number, time of first infection, and initial susceptible population, the study captures both the pace and persistence of Mpox spread, while critically assessing the effectiveness of national public health responses. Key findings reveal a paradox in North America: Canada exhibited a high transmission rate but a low reproduction number, indicating an elevated transmission potential per contact alongside limited secondary spread. This is likely due to concurrent containment measures or behavioral factors. In contrast, the United States, despite having a lower initial transmission rate, recorded a higher reproduction number. Similarly, Germany exhibited a similar risk trajectory, with elevated reproductive numbers despite robust infrastructure. The cases in the USA and Germany are likely due to systemic health and socio-political policy gaps and delayed behavior-targeted interventions, particularly in the population of men having sex with men (MSM). In Latin America, countries such as Peru and Mexico suffered disproportionately, likely due to limited access to healthcare, which compounded transmission dynamics and reproductive potential. Our study demonstrates that effective Mpox control is not solely dependent on health infrastructure, but also on behavioral targeting, equity, and adaptive health governance. This calls for cross-country and intercontinental collaborations towards combating current and future health shocks, including epidemics.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".