---
language: en
license: mit
task_categories:
- tabular-classification
tags:
- long-covid
- multi-omics
- recovery
- systems-biology
- sios
- interaction-geometry
size_categories:
- n<1K
pretty_name: Long Covid Recovery Interaction Geometry
---

# What this dataset does

This dataset tests whether failed Long Covid recovery is better predicted by interaction geometry than by any single biological burden signal.

The target is not disease diagnosis.

The target is failed recovery.

# Core stability idea

A patient may show high biological burden and still recover if repair capacity remains strong and omics layers stay coherent.

A patient may show moderate burden and fail to recover if repair capacity weakens and biological layers begin to diverge.

The central hypothesis is:

```text
failed recovery = burden × disagreement × weak repair

Prediction target
label = 1

means failed or stuck recovery trajectory.

label = 0

means recovered or stable recovery trajectory.

Row structure

Each row represents a synthetic patient recovery state.

Columns:

scenario_id
ifn_signature
cytokine_activation
acute_phase_proteins
mitochondrial_stress
lipid_disruption
microbiome_instability
oxidative_stress
repair_response
symptom_score
label
Biological interpretation

The dataset is biology-shaped but synthetic.

It is designed to test whether models can detect interaction structure across immune, metabolic, microbiome, oxidative, and repair signals.

It does not claim patient-level validation.

Files
data/train.csv
data/test.csv
scorer.py
README.md
Evaluation

Submit predictions as:

scenario_id,prediction
LC101,0
LC102,0
LC103,1

Run:

python scorer.py predictions.csv data/test.csv

The scorer reports:

accuracy
precision
recall
f1
confusion matrix
Structural Note

This dataset is part of the Clarus / SIOS synthetic benchmark series.

It is designed to test whether models can identify latent stability geometry rather than rely on single-variable correlation.

The hidden structure is not intended to represent a clinical rule.

The purpose is to evaluate whether a model can reason across interacting biological signals.

Production Deployment

This dataset should not be used for clinical decision-making.

A production version would require validated patient-level Long Covid multi-omics data, longitudinal outcomes, and independent external testing.

Enterprise & Research Collaboration

This benchmark can be extended into real-data research using public or partner datasets containing transcriptomics, proteomics, metabolomics, immune profiling, symptom trajectories, and recovery outcomes.

License

MIT