Pathogen Variant Surveillance
When to use
Any time an answer depends on what a pathogen population looks like now: which lineages are
circulating, whether one is growing, what a lineage name currently means, or whether an assay
target still matches.
The rule
Never state what is circulating, and never write a lineage name, from memory.
Three things go wrong at once, and only the first is an ordinary knowledge-cutoff problem:
- Names post-date training. The Pango designation list carries over 6,200 names and grows
continuously.
- The nomenclature is a live data structure, not a convention.
XFG is a recombinant that
only resolves through alias_key.json; PQ.17 unaliases to XDV.1.5.1.1.8.1.17. Neither
expansion is derivable by reasoning — the mapping is a file that changes.
- Prior knowledge gets retracted, not just outdated. 294 names in the current
lineage_notes.txt are withdrawn or redesignated. PC.2 is now LF.7.9; XFG.20 was
withdrawn outright. A remembered lineage fact is not merely stale, it can be actively wrong.
Every number this skill reports is a count returned by a live instance, stamped with the data
version it came from.
Scope
Surveillance data analysis for research. This skill describes sequences that were collected and
submitted; it does not produce clinical interpretations, outbreak-response recommendations, or
public-health guidance, and sequence counts are not case counts.
Instances
One API shape covers every pathogen. --instance names a verified deployment; --base-url
reaches any other LAPIS instance.
| Instance | Host | Lineage column | Indexed |
|---|
sars-cov-2 | lapis.cov-spectrum.org (open GenBank data) | pangoLineage | yes |
h5n1, h3n2, h1n1pdm, influenza-a | lapis.genspectrum.org | clade | no |
rsv-a, rsv-b, mpox, measles, dengue, west-nile, hmpv, ebola-zaire, ebola-sudan, cchf | lapis.pathoplexus.org | varies | varies |
Field names differ per instance and are never assumed. Every script reads
/sample/databaseConfig at run time and picks the collection-date, submission-date and lineage
columns from what the instance actually declares. dateFrom= is correct on SARS-CoV-2 and a hard
400 on H5N1, whose collection date is sampleCollectionDateRangeLower.
Scripts
cd skills/pathogen-variant-surveillance/scripts
| Script | Question answered |
|---|
resolve_lineage.py | Does this name still exist, what does it expand to, what is it descended from? |
lineage_prevalence.py | What share of sequences is this lineage, week by week, and is it growing? |
mutation_profile.py | What mutations does it carry, and how does it differ from another lineage? |
reporting_lag.py | How far back does the data have to go before it can be trusted? |
All four take --format table|tsv|json and print provenance (instance, data version, resolved
field names, filters) to stderr, so > out.tsv keeps the data clean and the provenance visible.
Start from the data, not from a remembered list
python3 lineage_prevalence.py --top 5 --where country=USA --weeks 12
note: discovered the 5 most common pangoLineage values in the window:
XFG.1.1, XFG.23.1.3, PY.1.1.1, XFJ.3.1.2, PQ.17
This is the right first command for "what is circulating". Naming lineages up front presumes you
already know which ones matter, which is the assumption this skill exists to remove.
Check a name before using it
python3 resolve_lineage.py XFG.23.1.3 PQ.17 PC.2 NOTALINEAGE
query status unaliased parent recombinant_of descendants sequences detail
XFG.23.1.3 current XFG.23.1.3 XFG.23.1 LF.7+LP.8.1.2 6 317 S:A1174V, on C29137T branch
PQ.17 current XDV.1.5.1.1.8.1.17 NB.1.8.1 23 931 Alias of XDV.1.5.1.1.8.1.17
PC.2 withdrawn B.1.1.529.2.86.1.1.16.1.7.2.1.2 LF.7.2.1 4 25 now LF.7.9; Redesignated as LF.7.9
NOTALINEAGE unknown NOTALINEAGE 0 n/a no such name in the live nomenclature
(detail abridged; each real row also cites the lineage proposal it came from.)
Exit code is 1 if any name is withdrawn or unknown, so it gates a manuscript's lineage list.
Note PC.2: withdrawn upstream, yet 25 sequences still carry the label because the instance's
assignments lag designation. Both facts are true and both matter.
Prevalence and growth
python3 lineage_prevalence.py "XFG.1.1*" "XFJ*" --where country=USA --weeks 16 --growth
lineage week n total proportion ci_low ci_high coverage
XFG.1.1* 2026-05-04 42 80 0.5250 0.4170 0.6308 ok
XFG.1.1* 2026-06-15 3 49 0.0612 0.0210 0.1652 ok
XFG.1.1* 2026-06-29 1 30 0.0333 0.0059 0.1667 low
XFG.1.1* 2026-07-13 0 0 low
Proportions carry Wilson intervals because surveillance weeks are small. Weeks whose denominator
has not filled in yet are flagged low and excluded from the growth fit unless
--include-incomplete.
The window is widened to whole ISO weeks, and says so when it does. A window starting mid-week
would give a first row covering three days and a last row covering four, neither comparable to the
full weeks between them.
--growth reports a weighted least-squares slope of log-odds against time. It is descriptive:
it absorbs every change in who is sequencing, where, and how fast they report. It is not a fitness
or transmissibility estimate. No slope is printed for a lineage with too few observations — see the
trap table for why that guard exists.
Mutations, and whether an assay still matches
python3 mutation_profile.py "XFJ*" --versus "XFG*" --gene S --since 2026-01-01
mutation gene position verdict prop_a prop_b n_a n_b
S:L441R S 441 gained 1.000 0.000 66 0
S:A475V S 475 gained 1.000 0.000 68 0
S:K444R S 444 lost 0.000 0.996 0 5031
S:Q493E S 493 lost 0.000 0.998 0 5359
Works the same on a segmented genome — --instance h5n1 --gene HA or --gene seg4. Use
--nucleotide for primer and probe questions, where the codon is not the unit that matters.
Decide how far back to trust
python3 reporting_lag.py --where country=USA
lag_days mean_complete min_complete max_complete cohorts
14 0.456 0.332 0.557 6
30 0.677 0.580 0.822 6
60 0.868 0.802 0.949 6
90 0.939 0.916 1.000 6
90% of a cohort has arrived by 90 days. Trust collection dates up to 2026-04-28; treat anything
later as provisional.
Run this before quoting any recent prevalence. The curve differs sharply by pathogen and
country: on H5N1 the same measurement returns 0% complete at 14 days and 15% at 30 days, so a
"current" H5N1 picture is effectively blind for two months.
Traps that produce silently wrong answers
All verified against the live API on 2026-07-27. These are why this skill ships scripts rather
than a recipe; full detail in references/lapis-api.md.
| Trap | Consequence |
|---|
| A bare lineage name excludes its descendants | pangoLineage=XFG returns 4 sequences; XFG* returns 640 |
A trailing * needs a lineage index | On H5N1 clade=2.3.4.4b returns 62,413 and clade=2.3.4.4b* returns 0 — the same syntax, the opposite meaning |
| Field names are per-instance | dateFrom is a 400 on H5N1; the collection date is sampleCollectionDateRangeLower |
Only date-typed fields take ranges | H5N1 types sampleCollectionDate as a string, so it has no From/To keys at all |
| Recent weeks are not a sample of what circulated | They are a sample of whoever reports fastest; only 29% of a US cohort arrives within 7 days |
| LAPIS roots recombinants | Asking it for XFG's parents returns nothing; only alias_key.json records XFG = LF.7 + LP.8.1.2 |
| Withdrawn names persist in the data | PC.2 was redesignated LF.7.9 upstream while sequences still carry PC.2 |
| An unknown name fails loudly only when indexed | Indexed columns reject a typo with a 400; unindexed columns answer 0 |
Mutation proportion is over coverage | Not over all matching sequences — a poorly covered site can show 1.000 on very few reads |
/sample/aggregated rejects limit/orderBy | The result has no inherent ordering; sort client-side |
Reporting results
State the instance, the data version, the filters, and the window — a prevalence figure without
them cannot be reproduced, because the underlying database changes daily. Give counts alongside
proportions, quote the interval, and say explicitly when a window is too recent to support an
estimate. "No reliable estimate for the last six weeks" is a legitimate and often correct answer.
References
references/lapis-api.md — endpoints, filter grammar, per-instance schema differences, the
instance registry, and every verified trap in full.references/lineage-nomenclature.md — Pango aliases and recombinants, designation churn,
Nextstrain clades, WHO labels, influenza clades, H5N1 clades and genotypes, and how the naming
systems map onto each other.references/surveillance-caveats.md — reporting lag, sampling and ascertainment bias, choosing
a denominator, interval and growth interpretation, and the conclusions this data cannot support.
