Pathogen Variant Surveillance

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:

1. Names post-date training. The Pango designation list carries over 6,200 names and grows continuously.
2. 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.
3. Prior knowledge gets retracted, not just outdated. 294 names in the current lineage_notes.txt are withdrawn or redesignated. PC.2 is now LF.7.9; XFG.20 was withdrawn outright.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.

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

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

# no names: discover what is actually circulating in the window
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.

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.