🦠💉🌍 `seroepi`¶

seroepi is a comprehensive Python toolkit and interactive Shiny dashboard for the epidemiological, geospatial, and genotypic analysis of pathogen isolates.

Built seamlessly on top of pandas, it is specifically designed to ingest output from genomic pipelines (like Kleborate and Pathogenwatch), calculate statistical burdens, map spatial transmissions, and computationally design optimal vaccine formulations.

✨ What You Can Do¶

Robust Data Stewardship: Validate inputs seamlessly against our UnifiedIsolateSchema (powered by Pandera) to ensure completely standardized genotypic, spatial, and temporal datasets.
Interactive Dashboarding: Launch a beautifully designed, dark-mode Shiny app to explore your data, train models, and generate publication-ready plots instantly.
Smart Pandas Accessors: Clean coordinates, query AMR genes, and generate epidemic curves natively using df.geo, df.geno, df.epi, and df.qc.
Robust Statistical Modeling: Estimate global and regional prevalence using Frequentist, Bayesian MCMC/SVI, and Gaussian Process (GP) Spatial models.
Vaccine Formulation Engine: Use rigorous Leave-One-Out (LOO) cross-validation to algorithmically identify the most stable and high-coverage target antigens (e.g., K-loci) for vaccine design.
Outbreak & Transmission Clustering: Instantly generate transmission networks and spatial cliques using SNP distance matrices and spatiotemporal thresholds.

📦 Installation¶

You can install seroepi directly from PyPI. We highly recommend using uv for lightning-fast installations, but standard pip works perfectly too.

# Using uv (Recommended)
uv pip install seroepi

# Using standard pip
pip install seroepi

Optional Dependencies¶

To unlock the advanced Bayesian models, Gaussian Processes, and Plotly visualizations, install the optional dependencies:

uv pip install seroepi[models,plot]

📖 Nomenclature: Traits vs. Targets¶

To ensure clarity across epidemiological modeling and vaccine design, seroepi strictly defines: * Trait: The overarching epidemiological variable or genomic locus being modeled (e.g., K_locus, O_locus, amr_KPC). * Target: The specific variant or level within that trait (e.g., K1, K2, K3).

When aggregating data, you specify the overarching Trait column, and the resulting DataFrames will standardize the individual variants into a `target` column for unified downstream processing.¶

💻 Python API Quickstart¶

If you prefer working in Jupyter Notebooks or Python scripts, seroepi extends standard Pandas DataFrames to make bioinformatics workflows effortless.

1. Data Ingestion, Validation & Spatial Cleaning¶

import pandas as pd
from seroepi.io import PathogenwatchKleborateParser
import seroepi.accessors  # Magically registers .epi, .geo, .geno, .qc

# Load your Kleborate output and optional Metadata
# Parse, merge, and strictly validate against the UnifiedIsolateSchema
df = PathogenwatchKleborateParser.parse(
    pd.read_csv("kleborate_results.csv"),
    meta_df=pd.read_csv("metadata.csv"),
    meta_kwargs={"id_col": "sample_id", "country_col": "country"}
)

# Automatically impute missing coordinates based on Country names!
df = df.geo.standardize_and_impute()

2. Genomic Clustering¶

from seroepi.dist import Distances

# Load a pairwise SNP matrix (e.g., from Pathogenwatch)
dist = Distances.from_pathogenwatch("distances.csv")

# Find isolates separated by ≤ 20 SNPs
clusters = dist.connected_components(threshold=20)

# Merge straight back into your dataframe
df = df.join(clusters, on='sample_id')

3. Prevalence Estimation & Plotting¶

from seroepi.estimators import FrequentistPrevalenceEstimator

# Aggregate the data to find the prevalence of K-loci across different countries
agg_df = df.epi.aggregate_prevalence(stratify_by=['country'], trait_col='K_locus')

# Fit the estimator
estimator = FrequentistPrevalenceEstimator(method='wilson')
results = estimator.calculate(agg_df)

# Generate a publication-ready Plotly Forest Plot
fig = results.plot('forest')
fig.show()

4. Algorithmic Vaccine Formulation¶

from seroepi.formulation import CVFormulationDesigner

# Design a 6-valent vaccine, using 'country' as the cross-validation holdout
designer = CVFormulationDesigner(valency=6, n_jobs=-1)
designer.fit(estimator, agg_df, loo_col='country')

# View the most stable optimal targets
optimal_vaccine = designer.formulation_
print(optimal_vaccine.get_formulation())

📚 Documentation¶

For a complete deep-dive into the available methods, classes, and architectural concepts, please refer to the fully documented API Reference (Automatically generated).

🦠💉🌍 seroepi¶