Forecasting with `antigen-prime` simulated data

This repository contains code, scripts, analysis notebooks, and data for forecasting variant frequencies using simulated pathogen evolution data from antigen-prime and growth advantage models from evofr.

Overview

The workflow typically follows these steps:

Parse antigen-prime simulation outputs using parse_sim_outputs.py
Prepare antigen data using prep_antigen_data.py
Assign variants using assign_all_variants.py
Create training datasets with make_training_data.py
Run forecasting models using either run_model.py directly or sbatch_models.py for SLURM-based environments
Score and evaluate models:
- score_models.py for frequency estimates
- score_growth_rates.py for growth rate estimates

Installation

# Clone the repository
git clone https://github.com/matsengrp/antigen-forecasting.git
cd antigen-forecasting

# Install dependencies with mamba
mamba env create -f environment.yaml

# Activate the environment
mamba activate antigen

# Install the package
pip install -e .

Data

The primary dataset used in this analysis is in data/flu-final/, which contains:

time-stamped/ - Sequence and case counts at bi-annual time points (April 1 and October 1)
antigen-outputs/ - Processed simulation outputs
variant-assignment/ - Variant assignment results from phylogenetic and sequence-based methods
tips_with_variants.tsv - Tip data with assigned variants

Usage

All scripts are in the scripts/ directory.

Orchestrated single-simulation run

The end-to-end pipeline (parse → variant assignment → prep → training data → models → scoring) is wrapped by run_pipeline.py. It executes all seven steps in DAG order with idempotent re-runs gated by per-step sentinel files; re-running an already-complete pipeline marks every step skipped.

python scripts/run_pipeline.py \
    --sim-path antigen-experiments/experiments/<exp>/<param-set>/run_0/ \
    --build flu-final \
    --config configs/pipeline_config.yaml

Options:

--sim-path - Path to a run_N/ directory under antigen-experiments/
--batch-name / --build - Mutually exclusive required group. Use --batch-name for batch runs (build derived as <batch>/<sim-id>); use --build for single-dataset mode (e.g. flu-final)
--config - Path to configs/pipeline_config.yaml (sources numpyro_seed, models, locations, etc.)
--skip-variant-assignment - Run only the antigenic k-means baseline; skip the t-SNE and phylogenetic variant pipelines
--skip-forecasting - Skip make_training_data, run_model, score_models, score_growth_rates
--dry-run - Print planned step sequence with skip decisions; do not execute
--force - Ignore sentinels and rerun every step; removes affected outputs first (never deletes inputs)
--set-thread-caps - Pin OMP_NUM_THREADS=MKL_NUM_THREADS=OPENBLAS_NUM_THREADS=1 and JAX_PLATFORMS=cpu in subprocess env (parent runners with --max-parallel > 1 should pass this)

Outputs:

All per-step files under data/<build>/ and results/<build>/ per SimulationPaths
results/<build>/pipeline.log - TSV with columns step, start_time (ISO 8601), end_time, duration_sec, status (ok | skipped | failed), command

The remaining sections document each step individually for users who want to invoke them directly.

1. Parse Simulation Outputs

The parse_sim_outputs.py script parses raw antigen-prime simulation outputs (run-out.tips) into pipeline-ready CSV/FASTA artifacts. Replaces the manual notebook step previously used for this purpose.

python scripts/parse_sim_outputs.py --sim-path path/to/run_N/ --output-dir data/build/antigen-outputs/

Options:

--sim-path - Path to a run_N/ directory under antigen-experiments/
--output-dir - Directory to write parsed outputs (typically data/<build>/antigen-outputs/)

Outputs:

tips.csv - Full parsed tips with country column added (no dedup)
unique_tips.csv - Deduplicated on name then nucleotideSequence (in this exact order)
unique_sequences.fasta - FASTA of unique sequences
out_timeseries.csv - Symlink to run_N/out_timeseries.csv (copy fallback)

Example:

python scripts/parse_sim_outputs.py \
    --sim-path antigen-experiments/experiments/<exp>/<param-set>/run_0/ \
    --output-dir data/flu-final/antigen-outputs/

2. Prepare Antigen Data

The prep_antigen_data.py script prepares antigen-prime simulated data for variant frequency forecasting.

python scripts/prep_antigen_data.py -t path/to/tips.csv -c path/to/cases.csv -o output/directory

Options:

-t, --tips - Path to the tips file
-c, --cases - Path to the cases file
-o, --output - Path to the output directory for prepped sequence and case counts
-sd, --start_date - Start date for the analysis
-v, --variant-col-name - Name of the column containing the variant information
-m, --variant-mapping - Path to variant mapping file (joins on nucleotideSequence)
-n, --normalize_cases - Normalize case counts to report cases per 100k hosts
--deme_population_size - Population size of the deme

Example:

python scripts/prep_antigen_data.py \
    -t data/antigen_h3n2_sim/antigen-outputs/tips.csv \
    -c data/antigen_h3n2_sim/antigen-outputs/cases.csv \
    -o data/antigen_h3n2_sim/time-stamped/truth/ \
    -sd 2022-01-01 -v variant_name --normalize_cases

3. Assign Variants

The assign_all_variants.py script assigns variant labels using three methods: antigenic (k-means), sequence-based (t-SNE), and phylogenetic (Augur clades).

python scripts/assign_all_variants.py \
    --tips data/build/tips.tsv \
    --fasta data/build/sequences.fasta \
    --output data/build/tips_with_variants.tsv \
    --work-dir data/build/variant_assignment/

Options:

--tips - Path to the tips TSV file. Must be deduplicated on name and nucleotideSequence (pass unique_tips.csv from parse_sim_outputs.py, not the full tips.csv); the script asserts this on entry to prevent silent divergence from the canonical name -> nucleotideSequence two-step dedup.
--fasta - Path to the sequences FASTA file
--output - Path to save the combined tips DataFrame with variant columns
--work-dir - Working directory for intermediate files

4. Create Training Data

The make_training_data.py script splits variant and count data into training windows.

python scripts/make_training_data.py -s path/to/sequences.csv -c path/to/cases.csv -o output/directory

Options:

-s, --sequences - Path to the variant counts file
-c, --cases - Path to the case counts file
-o, --output - Path to the output directory
--window-size - Size of the training window in days
--buffer-size - Number of days to remove from the training set before the analysis date
--config-path - Path to dump the training configuration file

After all per-date subdirectories are written, a MANIFEST.tsv file (columns: date, seq_counts_path, case_counts_path) is emitted at the top of the output directory. run_pipeline.py uses this as the step sentinel.

Example:

python scripts/make_training_data.py \
    -s data/antigen_h3n2_sim/time-stamped/truth/sequences.csv \
    -c data/antigen_h3n2_sim/time-stamped/truth/cases.csv \
    -o data/antigen_h3n2_sim/training/ \
    --window-size 90 --buffer-size 14 \
    --config-path configs/training_config.yaml

5. Run Forecasting Models

Option A: Run a single model

The run_model.py script fits a specified forecasting model.

python scripts/run_model.py -d data/directory -c country_name -m model_name -o output/directory

Options:

-d, --data_path - Path to the data directory
-c, --country - Country/deme to fit the model to
-m, --model - Model to fit (e.g., FGA, NAIVE)
-o, --output_dir - Directory to save the model results
--forecast_L - Number of days to forecast
--seed_L - Number of days to seed the forecast with
--config - Path to JSON configuration file with model parameters
--naive-full-window - Use full training window for NAIVE model (overrides config)

Example:

python scripts/run_model.py \
    -d data/antigen_h3n2_sim/time-stamped/2025-10-01/ \
    -c north -m FGA \
    -o results/antigen_h3n2_sim/estimates/ \
    --forecast_L 28 --seed_L 7

Option B: Run multiple models via SLURM

The sbatch_models.py script submits forecasting model inference jobs via SLURM.

python scripts/sbatch_models.py -b build_name -c configs/benchmark_config.yaml

Options:

-b, --build - Name of the build of the desired analysis
-c, --config - Path to the YAML configuration file

Example:

python scripts/sbatch_models.py -b antigen_h3n2_sim -c configs/benchmark_config.yaml

6. Evaluate Model Performance

Score frequency predictions

The score_models.py script computes frequency prediction scores with variant filtering.

python scripts/score_models.py \
    --config configs/benchmark_config.yaml \
    --truth-set path/to/truth.csv \
    --estimates-path path/to/estimates/ \
    --output-path results/scores/

Options:

--config - Path to the configuration file with scoring parameters
--truth-set - Path to the truth set of sequences
--estimates-path - Path to the model estimates directory
--output-path - Path to save the output scores
--verbose - Enable verbose logging
--log-file - Path to log file (optional)

Score growth rate predictions

The score_growth_rates.py script performs comprehensive growth rate analysis.

python scripts/score_growth_rates.py \
    --config configs/benchmark_config.yaml \
    --build build_name \
    --output-dir results/growth_rates/

Options:

--config - Path to the configuration file
--build - Build name (e.g., flu-simulated-150k-samples)
--output-dir - Directory to save the analysis outputs

Complete Pipeline Example

Here's an example of running the complete pipeline:

# 1. Parse antigen-prime simulation outputs
python scripts/parse_sim_outputs.py \
    --sim-path antigen-experiments/experiments/<exp>/<param-set>/run_0/ \
    --output-dir data/antigen_h3n2_sim/antigen-outputs/

# 2. Prepare antigen data
python scripts/prep_antigen_data.py \
    -t data/antigen_h3n2_sim/antigen-outputs/tips.csv \
    -c data/antigen_h3n2_sim/antigen-outputs/out_timeseries.csv \
    -o data/prepped/ -sd 2022-01-01 -v variant_name

# 3. Assign variants
python scripts/assign_all_variants.py \
    --tips data/prepped/tips.tsv \
    --fasta data/antigen_h3n2_sim/antigen-outputs/unique_sequences.fasta \
    --output data/prepped/tips_with_variants.tsv \
    --work-dir data/prepped/variant_assignment/

# 4. Create training data
python scripts/make_training_data.py \
    -s data/prepped/seq_counts.tsv -c data/prepped/case_counts.tsv \
    -o data/training/ --window-size 90

# 5. Run models (individual or batch)
python scripts/run_model.py \
    -d data/training/2025-10-01/ -c north -m FGA \
    -o results/estimates/
# Or with SLURM
python scripts/sbatch_models.py -b antigen_h3n2_sim -c configs/benchmark_config.yaml

# 6. Score frequency predictions
python scripts/score_models.py \
    --config configs/benchmark_config.yaml \
    --truth-set data/prepped/seq_counts.tsv \
    --estimates-path results/estimates/ \
    --output-path results/scores/

# 7. Score growth rate predictions
python scripts/score_growth_rates.py \
    --config configs/benchmark_config.yaml \
    --build antigen_h3n2_sim \
    --output-dir results/growth_rates/

Notes

Make sure all required input files are in the specified formats
For SLURM-based job submission, ensure your SLURM environment is properly configured
Check the generated configuration files to verify settings before running models
For programmatic path construction across pipeline stages, use antigentools.paths.SimulationPaths (from_sim_path for batch runs, from_build for single-dataset runs like flu-final) — it is the single source of truth for where files live

Clustering Evaluation Metrics

When comparing different clustering methods (e.g., antigenic, sequence-based, phylowave) for variant assignment, we evaluate them based on two main goals:

Goal 1 (Internal Evaluation): Assess how well cluster assignments explain the variance in the fitness distribution while penalizing the number of clusters used. We treat this as an ANOVA-like problem where cluster assignments are categorical predictors of fitness. Each cluster predicts fitness using its mean fitness value, and we evaluate both the variance explained (R²) and a version that penalizes methods for the number of clusters defined in the time window (adj-R²).

Goal 2 (External Evaluation): When considering the antigenic clusters as ground truth, evaluate how well the sequence-based clusters recover the same cluster structure. This measures whether alternative clustering methods (which may use different feature spaces) successfully identify the same underlying variant groupings.

Metrics Summary

Metric	Type	Range	Interpretation	Why We're Using It
R² (R-squared)	Internal	[0, 1], higher is better	Proportion of fitness variance explained by cluster assignments. 0 = no explanatory power, 1 = perfect prediction.	Goal 1: Directly measures how well cluster assignments explain fitness variation. Complements BIC by showing raw predictive power without complexity penalty.
Adj-R² (Adjusted R²)	Internal	(-∞, 1], higher is better	R² adjusted for number of clusters. Penalizes adding more clusters. Can be negative if model is very poor.	Goal 1: Alternative to BIC that also penalizes complexity. Useful for comparing methods with different cluster counts.
NMI (Normalized Mutual Information)	External	[0, 1], higher is better	Information-theoretic measure of cluster agreement. 0 = independent, 1 = identical clustering. Adjusts for chance.	Goal 2: Measures how much information about antigenic clusters is captured by sequence/phylowave clusters. Symmetric and normalized.
ARI (Adjusted Rand Index)	External	[-1, 1], higher is better	Measures pairwise agreement adjusted for chance. 0 = random labeling, 1 = perfect match. Negative = worse than random.	Goal 2: Gold standard for comparing clusterings. Adjusts for chance agreement and doesn't assume cluster size balance.

Usage Notes:

Internal Metrics (Goal 1): We report BIC since it directly optimizes the fitness prediction vs. complexity trade-off by penalizing the number of inferred clusters. R² and Adj-R² provide interpretable supplementary information about prediction quality. The approach treats clusters as categorical variables in an ANOVA-like framework where each cluster's mean fitness serves as the predicted value for all members of that cluster.

External Metrics (Goal 2): ARI is known as the most robust single metric for cluster agreement. NMI provides an information-theoretic perspective.

License

MIT License - see LICENSE for details.

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Forecasting with antigen-prime simulated data

Overview

Installation

Data

Usage

Orchestrated single-simulation run

Options:

Outputs:

1. Parse Simulation Outputs

Options:

Outputs:

Example:

2. Prepare Antigen Data

Options:

Example:

3. Assign Variants

Options:

4. Create Training Data

Options:

Example:

5. Run Forecasting Models

Option A: Run a single model

Options:

Example:

Option B: Run multiple models via SLURM

Options:

Example:

6. Evaluate Model Performance

Score frequency predictions

Options:

Score growth rate predictions

Options:

Complete Pipeline Example

Notes

Clustering Evaluation Metrics

Metrics Summary

Usage Notes:

License

Forecasting with `antigen-prime` simulated data