---
license: mit
library_name: flax
tags:
  - jax
  - flax
  - nnx
  - normalizing-flow
  - rational-quadratic-spline
  - quantile-regression
  - malaria
  - epidemiology
pipeline_tag: tabular-regression
---

# estiMINT RQS models

Conditional rational-quadratic spline (RQS) normalizing flows used by
[estiMINT](https://github.com/CosmoNaught/estiMINT) to predict quantiles of
malaria transmission intensity targets (EIR, human biting rate) from a single
transmission-intensity predictor plus intervention-coverage covariates.

Each model is a conditional normalizing flow: an MLP maps context features to
spline parameters, which transform a standard normal base distribution into
the target's distribution. Sampling a quantile `q` gives calibrated predictive
intervals in addition to a point (median) estimate.

## Available models

| Model | Predictor | Target |
|---|---|---|
| `hbr_y9-eir` | `hbr_y9` | `eir` |
| `prev_y9-eir` | `prev_y9` | `eir` |
| `eir-hbr_y9` | `eir` | `hbr_y9` |

## Covariates

Every model takes the same seven raw covariates: the predictor column first,
then six intervention-coverage covariates.

| Position | Name | Notes |
|---|---|---|
| 0 | `<predictor>` | `eir`, `prev_y9`, or `hbr_y9` — whichever the model is keyed on |
| 1 | `dn0_use` | |
| 2 | `Q0` | |
| 3 | `phi_bednets` | |
| 4 | `seasonal` | |
| 5 | `itn_use` | |
| 6 | `irs_use` | |

So `hbr_y9-eir` expects
`hbr_y9, dn0_use, Q0, phi_bednets, seasonal, itn_use, irs_use`.

Pass raw, unstandardized values — standardization (and log10 transforms where
applicable) is applied internally. Predictions are returned on the original
(non-log) scale, clipped at 0.

## Usage

```python
from estimint.v2.models.rqs import ConditionalRQS

# from a local export directory
artifact = ConditionalRQS.from_pretrained("artifacts/hbr_y9-eir", predictor="hbr_y9", target="eir")

# from this Hub repo
artifact = ConditionalRQS.from_pretrained("<org>/<repo>", predictor="hbr_y9", target="eir")

X_raw = [
    {"hbr_y9": 12.4, "dn0_use": 0.5, "Q0": 0.92, "phi_bednets": 0.80,
     "seasonal": 0, "itn_use": 0.40, "irs_use": 0.00},
    {"hbr_y9": 3.1, "dn0_use": 0.3, "Q0": 0.90, "phi_bednets": 0.70,
     "seasonal": 1, "itn_use": 0.20, "irs_use": 0.10},
]

artifact.predict(X_raw)                 # median prediction, shape (2,)
artifact.quantile(X_raw, 0.9)           # single quantile
artifact.interval(X_raw, alpha=0.10)    # (lower, upper) 90% predictive interval
```

`X_raw` may be:

- a **list of dicts**, one dict per row, keyed by the covariate names above.
  Key order does not matter — the artifact reorders each row into the
  training order. Each dict must contain exactly the seven covariates;
  missing or unexpected keys raise an error.
- a **single dict**, treated as one row (results still come back as
  length-1 arrays).
- a **`(batch, 7)` array** of raw values, already in the order above.

Note: `interval()` currently returns the raw `[alpha/2, 1-alpha/2]` quantile
band; the conformal calibration offset computed during training is not yet
carried over to exported artifacts, so intervals from `from_pretrained` models
are not conformally corrected.

## License

MIT, matching the parent [estiMINT](https://github.com/CosmoNaught/estiMINT) repo.