VIRUS-MVP: a framework for comprehensive surveillance of viral mutations and their functional impacts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As viruses evolve, they accumulate genetic mutations that can influence disease severity, transmissibility, and the effectiveness of vaccines and therapeutics. Real-time tracking of viral mutations and their functional impacts is essential to understand these changes and assess their implications for public health responses. VIRUS-MVP is an interactive, portable platform designed for the comprehensive surveillance of viral mutations. Initially developed for SARS-CoV-2, it now fully supports mpox and is expanding to include influenza and RSV. The platform links viral mutations to functional annotations, providing insights into their predicted effects on viral infectivity, immune evasion, and protein functionality. It features an interactive interface for visualizing mutation distributions, a modular and reproducible genomics workflow, and a curated annotation resource that captures known impacts on viral proteins and host interactions. Users can also import custom functional annotations to tailor analyses to specific research needs or emerging pathogens. Developed collaboratively with public health and academic partners, VIRUS-MVP enhances understanding of viral evolution and its public health impact by bridging genomic data with biological insights. The platform is open-source, adaptable, and accessible on GitHub.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it