Human outbreak detection and best practice MPXV analysis and interpretation with squirrel
Bibliographic record
Abstract
High numbers of reported mpox cases and recent identification of multiple sustained human outbreaks of mpox virus (MPXV) have highlighted the need for robust, best-practice genomic surveillance tools. In light of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, many labs across the globe developed the capacity to do virus genome sequencing; however, MPXV presents additional analytical challenges due to its large genome size, tracts of low-complexity or repeat regions, genetically distinct clades, and the need to perform bespoke apolipoprotein B mRNA editing enzyme catalytic polypeptide-like 3 (APOBEC3)-mutation reconstruction. We present squirrel (Some Quick Reconstruction to Resolve Evolutionary Links), an open source bioinformatic tool that can perform clade-aware alignment, mutation quality assessment, phylogenetic inference, and automated APOBEC3-mutation classification on branches of the phylogeny. Squirrel can be run on the command line or launched through the EPI2ME graphical user interface through the squirrel-nf workflow, enabling robust analysis without need for the command line. With the interactive output report produced and publication-ready APOBEC3-reconstruction visualization, squirrel enables researchers to distinguish between zoonotic and sustained human outbreaks and help accurately inform public health responses.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".