Estimating the size of the monkeypox virus outbreak in Nigeria and implications for global control
Bibliographic record
Abstract
BACKGROUND: A multi-country outbreak caused by monkeypox virus (MPXV) has been unfolding across endemic and non-endemic countries since May 2022. Throughout April and May 2022, Nigeria reported 31 MPXV cases, of which 11 were confirmed via testing. In May 2022, three internationally exported cases of MPXV, presumed to have originated in Nigeria, were reported, suggesting that a larger than reported outbreak might be occurring in the country. METHODS: We used previously established methods to estimate the true size of the MPXV outbreak in Nigeria. We estimated the incidence rate of exported MPXV cases among all outbound international air travellers from Nigeria during the time period of April and May 2022, using forecasted air traveller volumes. We then applied this incidence rate to the entire population of Nigeria during April and May 2022 assuming that the rate of infection was the same in Nigeria for both travellers and the resident population. Information on the subset of population that were considered to be travellers was obtained from the United Nations World Tourism Organization (UNWTO). RESULTS: We estimated that there were approximately 4000 (N = 4013; 95% CI: 828-11 728) active cases of MPXV in Nigeria in April and May 2022. This is approximately 360-fold greater than the confirmed number and approximately 130-fold greater than the reported number of cases in Nigeria. CONCLUSION: Our findings suggest that a larger outbreak than is appreciated may be ongoing in Nigeria. The observed international spread of MPXV offers important insights into the scale of the epidemic at its origin, where clinical detection and disease surveillance may be limited. These findings highlight the need to expand and support clinical, laboratory, and public health capacity to enable earlier detection of epidemics of international significance.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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, 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".