Global burden of chikungunya virus infections and the potential benefit of vaccination campaigns
Bibliographic record
Abstract
The first vaccine against chikungunya virus (CHIKV) has now been licensed; however, due to a limited knowledge of the underlying global burden, its potential to reduce disease burden remains unknown. We used data from seroprevalence studies, observed cases and mosquito distributions to quantify the underlying CHIKV burden in 180 countries and territories, and we explored the potential impact of vaccination campaigns. We estimate that 104 countries have experienced CHIKV transmission, covering 2.8 billion people, and that, in epidemic settings, the mean duration between outbreaks is 6.2 years, with 8.4% of the susceptible population infected per outbreak. Globally, there are 35 million annual infections, mainly in Southeast Asia, Africa and the Americas. Assuming a vaccine efficacy against disease of 70% and a protection against infection of 40%, vaccinating 50% of individuals over 12 years of age in places and times where the virus circulates would avert 4,436 infections, 0.34 deaths and 17 disability-adjusted life years per 100,000 doses used. These findings highlight the global burden of chikungunya and the potential of CHIKV vaccination campaigns.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".