Modelling vaccination approaches for mpox containment and mitigation in the Democratic Republic of the Congo
Bibliographic record
Abstract
BACKGROUND: Mpox was first identified in the Democratic Republic of the Congo (DRC) in 1970. In 2023, a historic outbreak of mpox occurred in the country, continuing into 2024. Over 14 000 cases and 600 deaths were reported in 2023 alone, representing a major increase from previous outbreaks. The modified vaccinia Ankara vaccine (brand names JYNNEOS, Imvamune, and Imvanex) was used in the 2022 mpox outbreak in the USA and Europe. However, at the time of the study, vaccination had not been made available in the DRC. We aimed to inform policy and decision makers on the potential benefits of, and resources needed, for mpox vaccination campaigns in the DRC by providing counterfactual scenarios evaluating the short-term effects of various vaccination strategies on mpox cases and deaths, if such a vaccination campaign had been undertaken before the 2023-24 outbreak. METHODS: A dynamic transmission model was used to simulate mpox transmission in the DRC, stratified by age (<5, 5-15, and >15 years) and province. The model was used to simulate potential vaccination strategies, varying by age and region (endemic provinces, non-endemic provinces with historic cases, and all provinces) assessing the effect the strategies would have on deaths and cases in an epidemic year similar to 2023. In addition, we estimated the number of vaccine doses needed to implement each strategy. FINDINGS: Without vaccination, our model predicted 14 700 cases and 700 deaths from mpox over 365 days. Vaccinating 80% of all children younger than 5 years in endemic regions led to a 27% overall reduction in cases and a 43% reduction in deaths, requiring 10·5 million vaccine doses. Vaccinating 80% of all children younger than 5 years in all regions led to a 29% reduction in cases and a 43% reduction in deaths, requiring 33·1 million doses. Vaccinating 80% of children aged 15 years or younger in endemic provinces led to a 54% reduction in cases and a 71% reduction in deaths, requiring 26·6 million doses. INTERPRETATION: When resources are limited, vaccinating children aged 15 years or younger, or younger than 5 years, in endemic regions of the DRC would be the most efficient use of vaccines. Further research is needed to explore long-term effects of a one-time or recurrent vaccination campaign. FUNDING: Canadian Institutes of Health Research, Canadian International Development Research Centre, US Department of Defense (Defense Threat Reduction Agency, Mpox Threat Reduction Network), Global Affairs Canada (Weapons Threat Reduction Program), US Department for Agriculture (Agriculture Research Service, Non-Assistance Cooperative Agreement).
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".