Risk assessment and mitigation evaluation of future yellow fever outbreaks under different climate scenarios: Insight from a case study of Brazil
Bibliographic record
Abstract
BACKGROUND: Yellow Fever (YF), a disease typically transmitted to humans by infected mosquitoes, is endemic to regions such as South America. Climate change plays a crucial role in exacerbating the spread of YF. MODEL: We formulate a mathematical model of YF transmission with a case study of the region in the southeastern Brazil with a well documented 2017/2018 outbreak. We validate the model using historical data, then run simulations to generate projections of future outbreaks under different climate scenarios in 2050. We also evaluate the outcomes of different mitigation measures such as emergency vaccination programs. FINDINGS: Our results suggest that under all projected climate scenarios, increasing temperatures will yield a marked increase in the total number of cases. Under RCP 8.5, the basic human infection reproduction number will increase by 11.4%, and the cumulative infections will increase by 8.1%. The model predicts a similar increase under a moderate radiative forcing scenario. The introduction of additional emergency vaccination, at a rate of 8.0% (equivalent to 60% vaccination coverage over the course of 15 weeks) of the susceptible population per week, can reduce this increase of cumulative cases to approximately 4.9%. This effect of emergency vaccines will be equivalent to alternative public health interventions to reduce the mosquito-to-human disease transmission effective contact by approximately 23.0%. Increasing temperatures and rainfall due to climate change are projected to increase YF cases. Vaccination can be an important part of integrative mitigating measures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".