Chikungunya vaccine development, challenges, and pathway toward public health impact
Bibliographic record
Abstract
Chikungunya is a neglected tropical disease of growing public health concern with outbreaks in more than 114 countries in Asia, Africa, Americas, Europe, and Oceania since 2004. There are no specific antiviral treatment options for chikungunya virus infection. This article describes the chikungunya vaccine pipeline and assesses the challenges in the path to licensure, access, and uptake of chikungunya vaccines in populations at risk. Ixchiq (VLA1533/Ixchiq - Valneva) was the first licensed chikungunya vaccine by the US Food and Drug Administration in November 2023, European Medicines Agency in May 2024, and Health Canada in June 2024. Five chikungunya vaccine candidates (BBV87 - BBIL/IVI, MV-CHIK - Themis Bioscience, ChAdOx1 Chik - University of Oxford, PXVX0317 / VRC-CHKVLP059-00-VP - Bavarian Nordic, and mRNA-1388 - Moderna) are in development. Evidence on chikungunya disease burden alongside the public health and economic impact of vaccination are critical for decision-making on chikungunya vaccine introduction in endemic and epidemic settings. Further, global and regional stakeholders need to agree on a sustainable financing mechanism for manufacturing at scale to facilitate fair access and equitable vaccine distribution to at-risk populations in different geographic settings. This could partly be facilitated through obtaining consensus on scientific and regulatory principles for initial vaccine introduction and generating evidence on chikungunya burden and disease awareness among populations at risk. Specifically, this article advocates for the formation of a global chikungunya vaccine consortium that includes regulators, policymakers, sponsors, and manufacturers to assist in overcoming the global and local challenges for chikungunya vaccine licensure, policy, financing, demand generation, and access to at-risk populations.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".