Transmission Inference

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Five audit layers

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Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/epidemiological-genomics/transmission-inferenceThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Infers person-to-person transmission from pathogen genomes using outbreaker2, TransPhylo, phybreak, BadTrIP, SCOTTI, BEASTLIER, and SNP-distance / cluster-picker approaches (HIV-TRACE for HIV; transcluster). Defines…

---
name: bio-epidemiological-genomics-transmission-inference
description: Infers person-to-person transmission from pathogen genomes using outbreaker2, TransPhylo, phybreak, BadTrIP, SCOTTI, BEASTLIER, and SNP-distance / cluster-picker approaches (HIV-TRACE for HIV; transcluster). Defines outbreak clusters using pathogen-specific SNP thresholds (NOT a universal cutoff -- TB <=12 SNPs; MRSA <=15; C. difficile <=2; Klebsiella <=21), models within-host diversity and transmission bottlenecks, integrates contact-tracing data, distinguishes generation from serial interval, and attributes source via Bayesian source attribution (islandR). Use when investigating outbreaks for who-infected-whom, defining SNP-cluster outbreak definitions, accounting for unsampled intermediates, choosing between outbreaker2 (rich epi data) and TransPhylo (genomic-only after a dated phylogeny), running source attribution between host populations, calling HIV-TRACE thresholds appropriate to the local subtype, or distinguishing recent transmission from reactivation in TB or chronic HIV.
tool_type: mixed
primary_tool: TransPhylo
---

## Version Compatibility

Reference examples tested with: TransPhylo 1.4+ (R), outbreaker2 1.2+ (R), phybreak 0.5+ (R), BadTrIP via BEAST 2.7+ package manager, BEASTLIER via BEAST 1.10+, transcluster 1.0+ (R), HIV-TRACE 1.5+, snp-dists 0.8+, ape 5.8+ (R), igraph 1.6+ (R), TreeTime 0.11+, BactDating 1.1+ (R), BEAST 2.7.6+, lofreq 2.1+, deepSNV via Bioconductor 3.18+, pandas 2.2+, BioPython 1.84+.

Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('TransPhylo')`; `?inferTTree` to confirm arg names
- R: `packageVersion('outbreaker2')`; `?create_config` -- iteration count is set via `n_iter` in the `config` object, NOT as `iters` to `outbreaker()`
- Python: `pip show lofreq`; check whether deep variant calling supports the target MAF
- CLI: `snp-dists --help`; `hiv-trace --help`

If R rejects an argument, the function signature changed between minor releases; `?function_name` is authoritative.

# Transmission Inference

**"Who infected whom in this outbreak, and is this even an outbreak?"** -> Pick the question first (cluster definition vs WIWS who-infected-whom vs source attribution), then the method that fits the data (rich epi + dense sampling -> outbreaker2; sparse sampling + good dated tree -> TransPhylo; longitudinal within-host samples -> BEASTLIER / BadTrIP; rapid surveillance triage -> SNP-distance with pathogen-tuned threshold). Genomic distance is necessary but not sufficient for direction: two isolates 3 SNPs apart could be A->B, B->A, A->Unknown->B, or two-from-one common source. Direction inference requires temporal data, within-host diversity, contact-tracing data, or all three.

- R: `outbreaker2::outbreaker(data=outbreaker_data(dates=..., dna=..., w_dens=..., f_dens=..., ctd=...), config=create_config(n_iter=1e6))` -- dense outbreak with contact data
- R: `TransPhylo::inferTTree(ptree, mcmcIterations=1e5, w.shape=1.3, w.scale=10)` -- sparse outbreak from a dated tree
- CLI: `snp-dists -c gubbins.filtered_polymorphic_sites.fasta > pairwise.csv` -- pairwise SNP for cluster triage
- CLI: `hiv-trace --threshold 0.015` -- HIV cluster definition at the US-CDC default (subtype B); reconsider for non-B subtypes

## The Single Most Important Modern Insight -- There is no universal SNP cutoff for transmission

The pathogen-specific SNP threshold varies by 10x across taxa (TB <=12 SNPs, *C. difficile* <=2, MRSA <=15, *Salmonella* cgMLST <=5, *Klebsiella* <=21, SARS-CoV-2 not defined by SNP alone). Substitution rate, recombination, generation time, within-host diversity, and (for Mpox) APOBEC3 editing all vary by 100x. Walker 2013 *Lancet Infect Dis* 13:137 derived the TB <=12 SNP cutoff from UK Oxfordshire (low-transmission, contact-traced, household settings); applying the same threshold in Cape Town or Mumbai inflates apparent recent-transmission rates 2-5x because clonal isolates linked through long-past common ancestors get pooled with truly recent transmissions. Worby, Lipsitch & Hanage 2014 *PLoS Comput Biol* 10:e1003549 formally showed that within-host bacterial diversity puts an irreducible upper bound on the resolution of SNP-distance transmission-network reconstruction even with repeated sampling. Always cite the pathogen-specific source AND its derivation population; never apply a threshold outside its validated context without an explicit caveat. For TB / HIV / chronic infections, naive SNP cutoffs fail because of reactivation and within-host coalescence -- use TransPhylo or outbreaker2 with within-host-aware priors.

## Algorithmic Taxonomy

| Tool | Mechanism | Inputs | Output | Strength | Fails when |
|------|-----------|--------|--------|----------|------------|
| Pairwise SNP threshold (snp-dists; cluster picker) | Count SNPs between pairs; threshold + linkage | Core-SNP alignment | Adjacency at threshold | Fast; intuitive; standard for surveillance triage | Pathogen-specific cutoff; convergent evolution and recombination violate distance assumptions |
| HIV-TRACE (Kosakovsky Pond 2018 *Mol Biol Evol* 35:1812) | TN93 pairwise distance + threshold (default 1.5%) | HIV-1 pol or other gene | Cluster membership | CDC standard for US HIV surveillance | 1.5% threshold is US-CDC subtype B specific; under-clusters subtype C in southern Africa |
| outbreaker2 (Campbell 2018 *BMC Bioinformatics* 19:363) | MCMC; sequence + generation-interval + sampling-time + contact-tracing | Dated genomes + epi data | Posterior WIWS + unsampled intermediates + R_e | Integrates epi data explicitly; modular likelihood | ~100-200 cases practical limit; assumes one infection event per case (no within-host populations) |
| TransPhylo (Didelot 2017 *Mol Biol Evol* 34:997) | Coalescent within-host + birth-death between-host; colours a dated tree | Time-scaled tree + sampling dates | Posterior transmission tree + R_t + unsampled cases | Works from a tr

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

pip install pandas biopython

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

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1. 2026-10-19
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   first audit · source c8c1fc3a2b13

Questions about Transmission Inference

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