Human judgment forecasts of human monkeypox transmission and burden in non-endemic countries
Bibliographic record
Abstract
Background: An increase in reported human infections by the monkeypox virus (MPXV) has been observed in multiple non-endemic countries. Forecasts of transmission and disease burden associated with MPXV can support public health decision making. However, historical data that can be used to train computational forecasts is sparse. Here we show how crowdsourced human judgment can generate probabilistic predictions of the potential evolution of the international MPXV outbreak before robust computational models are prepared to provide such forecasts. Methods: We posed 8 questions associated with the monkeypox outbreak on the Metaculus forecasting platform. A total of 686 original and revised probabilistic predictions from 222 human forecasters were submitted to the forecasting platform from May 19th, 2022 to May 24, 2022. A performance based ensemble algorithm combined these individual predictions into ensemble forecasts. Findings: At time of writing, human judgment ensemble forecasts predict that the number of incident cases in the US, Canada, and Europe will continue to increase and the virus will continue to spread to multiple additional countries. Ensemble forecasts predict the World Health Organization will not declare human monkeypox a Public Health Emergency of International Concern before Dec 31, 2022.Interpretation: Human judgment forecasting is a rapid and readily adaptable approach that may improve situational awareness, synthesize available evidence, and meet public health needs as an outbreak evolves.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".