Test convolve mode in hospitaladmissionspy

docs/source/tutorials/day_of_the_week.qmd

@@ -43,7 +43,7 @@ inf_hosp_int = datasets.load_infection_admission_interval()
 # We only need the probability_mass column of each dataset
 gen_int_array = gen_int["probability_mass"].to_numpy()
 gen_int = gen_int_array
-inf_hosp_int = inf_hosp_int["probability_mass"].to_numpy()
+inf_hosp_int_array = inf_hosp_int["probability_mass"].to_numpy()
 ```
 
 2. Next, we defined the model's components:
@@ -56,7 +56,7 @@ import jax.numpy as jnp
 import numpyro.distributions as dist
 
 inf_hosp_int = deterministic.DeterministicPMF(
-    name="inf_hosp_int", value=inf_hosp_int
+    name="inf_hosp_int", value=inf_hosp_int_array
 )
 
 hosp_rate = metaclass.DistributionalRV(
@@ -77,14 +77,16 @@ from pyrenew.latent import (
 
 # Infection process
 latent_inf = latent.Infections()
+n_initialization_points = max(gen_int_array.size, inf_hosp_int_array.size) - 1
+
 I0 = InfectionInitializationProcess(
     "I0_initialization",
     metaclass.DistributionalRV(
         name="I0",
         distribution=dist.LogNormal(loc=jnp.log(100), scale=jnp.log(1.75)),
     ),
     InitializeInfectionsExponentialGrowth(
-        gen_int_array.size,
+        n_initialization_points,
         deterministic.DeterministicVariable(name="rate", value=0.05),
     ),
     t_unit=1,
@@ -201,11 +203,7 @@ hosp_model.run(
 out = hosp_model.plot_posterior(
     var="latent_hospital_admissions",
     ylab="Hospital Admissions",
-    obs_signal=np.pad(
-        daily_hosp_admits.astype(float),
-        (gen_int_array.size, 0),
-        constant_values=np.nan,
-    ),
+    obs_signal=daily_hosp_admits.astype(float),
 )
 ```
 
@@ -299,11 +297,7 @@ The new model with the day-of-the-week effect can be compared to the previous mo
 out = hosp_model.plot_posterior(
     var="latent_hospital_admissions",
     ylab="Hospital Admissions",
-    obs_signal=np.pad(
-        daily_hosp_admits.astype(float),
-        (gen_int_array.size, 0),
-        constant_values=np.nan,
-    ),
+    obs_signal=daily_hosp_admits.astype(float),
 )
 ```
 
@@ -314,10 +308,6 @@ out = hosp_model.plot_posterior(
 out_dow = hosp_model_dow.plot_posterior(
     var="latent_hospital_admissions",
     ylab="Hospital Admissions",
-    obs_signal=np.pad(
-        daily_hosp_admits.astype(float),
-        (gen_int_array.size, 0),
-        constant_values=np.nan,
-    ),
+    obs_signal=daily_hosp_admits.astype(float),
 )
 ```

docs/source/tutorials/hospital_admissions_model.qmd

@@ -118,17 +118,17 @@ inf_hosp_int = datasets.load_infection_admission_interval()
 # We only need the probability_mass column of each dataset
 gen_int_array = gen_int["probability_mass"].to_numpy()
 gen_int = gen_int_array
-inf_hosp_int = inf_hosp_int["probability_mass"].to_numpy()
+inf_hosp_int_array = inf_hosp_int["probability_mass"].to_numpy()
 
 # Taking a peek at the first 5 elements of each
-gen_int[:5], inf_hosp_int[:5]
+gen_int[:5], inf_hosp_int_array[:5]
 
 # Visualizing both quantities side by side
 fig, axs = plt.subplots(1, 2)
 
 axs[0].plot(gen_int)
 axs[0].set_title("Generation interval")
-axs[1].plot(inf_hosp_int)
+axs[1].plot(inf_hosp_int_array)
 axs[1].set_title("Infection to hospital admission interval")
 plt.show()
 ```
@@ -142,7 +142,7 @@ import jax.numpy as jnp
 import numpyro.distributions as dist
 
 inf_hosp_int = deterministic.DeterministicPMF(
-    name="inf_hosp_int", value=inf_hosp_int
+    name="inf_hosp_int", value=inf_hosp_int_array
 )
 
 hosp_rate = metaclass.DistributionalRV(
@@ -168,14 +168,15 @@ from pyrenew.latent import (
 
 # Infection process
 latent_inf = latent.Infections()
+n_initialization_points = max(gen_int_array.size, inf_hosp_int_array.size) - 1
 I0 = InfectionInitializationProcess(
     "I0_initialization",
     metaclass.DistributionalRV(
         name="I0",
         distribution=dist.LogNormal(loc=jnp.log(100), scale=jnp.log(1.75)),
     ),
     InitializeInfectionsExponentialGrowth(
-        gen_int_array.size,
+        n_initialization_points,
         deterministic.DeterministicVariable(name="rate", value=0.05),
     ),
     t_unit=1,
@@ -308,11 +309,7 @@ We can use the `Model` object's `plot_posterior` method to visualize the model f
 out = hosp_model.plot_posterior(
     var="latent_hospital_admissions",
     ylab="Hospital Admissions",
-    obs_signal=np.pad(
-        daily_hosp_admits.astype(float),
-        (gen_int_array.size, 0),
-        constant_values=np.nan,
-    ),
+    obs_signal=daily_hosp_admits.astype(float),
 )
 ```
 
@@ -381,22 +378,14 @@ axes.set_ylabel("Hospital Admissions", fontsize=10)
 plt.show()
 ```
 
-We can look at individual draws from the posterior distribution of latent infections:
-
-```{python}
-# | label: fig-output-infections
-# | fig-cap: Latent infections
-out2 = hosp_model.plot_posterior(
-    var="all_latent_infections", ylab="Latent Infections"
-)
-```
-
 We can also look at credible intervals for the posterior distribution of latent infections:
 
 ```{python}
 # | label: fig-output-infections-distribution
 # | fig-cap: Posterior Latent Infections
-x_data = idata.posterior["all_latent_infections_dim_0"]
+x_data = (
+    idata.posterior["all_latent_infections_dim_0"] - n_initialization_points
+)
 y_data = idata.posterior["all_latent_infections"]
 
 fig, axes = plt.subplots(figsize=(6, 5))
@@ -499,7 +488,7 @@ def compute_eti(dataset, eti_prob):
 
 fig, axes = plt.subplots(figsize=(6, 5))
 az.plot_hdi(
-    idata.prior_predictive["negbinom_rv_dim_0"] + gen_int.size(),
+    idata.prior_predictive["negbinom_rv_dim_0"],
     hdi_data=compute_eti(idata.prior_predictive["negbinom_rv"], 0.9),
     color="C0",
     smooth=False,
@@ -508,7 +497,7 @@ az.plot_hdi(
 )
 
 az.plot_hdi(
-    idata.prior_predictive["negbinom_rv_dim_0"] + gen_int.size(),
+    idata.prior_predictive["negbinom_rv_dim_0"],
     hdi_data=compute_eti(idata.prior_predictive["negbinom_rv"], 0.5),
     color="C0",
     smooth=False,
@@ -517,7 +506,7 @@ az.plot_hdi(
 )
 
 plt.scatter(
-    idata.observed_data["negbinom_rv_dim_0"] + gen_int.size(),
+    idata.observed_data["negbinom_rv_dim_0"],
     idata.observed_data["negbinom_rv"],
     color="black",
 )
@@ -533,7 +522,7 @@ And now we plot the posterior predictive distributions with a `{python} n_foreca
 ```{python}
 # | label: fig-output-posterior-predictive-forecast
 # | fig-cap: Posterior predictive admissions, including a forecast.
-x_data = idata.posterior_predictive["negbinom_rv_dim_0"] + gen_int.size()
+x_data = idata.posterior_predictive["negbinom_rv_dim_0"]
 y_data = idata.posterior_predictive["negbinom_rv"]
 fig, axes = plt.subplots(figsize=(6, 5))
 az.plot_hdi(
@@ -564,7 +553,7 @@ plt.plot(
     label="Median",
 )
 plt.scatter(
-    idata.observed_data["negbinom_rv_dim_0"] + gen_int.size(),
+    idata.observed_data["negbinom_rv_dim_0"],
     idata.observed_data["negbinom_rv"],
     color="black",
 )

pyrenew/convolve.py

@@ -165,3 +165,45 @@ def _new_scanner(
         return latest, (new_val, m_net1)
 
     return _new_scanner
+
+
+def compute_delay_ascertained_incidence(
+    p_observed_given_incident: ArrayLike,
+    latent_incidence: ArrayLike,
+    delay_incidence_to_observation_pmf: ArrayLike,
+) -> ArrayLike:
+    """
+    Computes incidences observed according
+    to a given observation rate and based
+    on a delay interval.
+
+    Parameters
+    ----------
+    p_observed_given_incident: ArrayLike
+        The rate at which latent incident counts translate into observed counts.
+        For example, setting ``p_observed_given_incident=0.001``
+        when the incident counts are infections and the observed counts are
+        reported hospital admissions could be used to model disease and population
+        for which the probability of a latent infection leading to a reported
+        hospital admission is 0.001.
+    latent_incidence: ArrayLike
+        Incidence values based on the true underlying process.
+    delay_incidence_to_observation_pmf: ArrayLike
+        Probability mass function of delay interval from incidence to observation,
+        where the :math:`i`th entry represents a delay of :math:`i`
+        time units, i.e. ``delay_incidence_to_observation_pmf[0]`` represents
+        the fraction of observations that are delayed 0 time unit,
+        ``delay_incidence_to_observation_pmf[1]`` represents the fraction
+        that are delayed 1 time units, et cetera.
+
+    Returns
+    --------
+    ArrayLike
+        The predicted timeseries of delayed observations.
+    """
+    delay_obs_incidence = jnp.convolve(
+        p_observed_given_incident * latent_incidence,
+        delay_incidence_to_observation_pmf,
+        mode="valid",
+    )
+    return delay_obs_incidence

pyrenew/latent/hospitaladmissions.py

@@ -9,6 +9,7 @@
 import numpyro
 
 import pyrenew.arrayutils as au
+from pyrenew.convolve import compute_delay_ascertained_incidence
 from pyrenew.deterministic import DeterministicVariable
 from pyrenew.metaclass import RandomVariable, SampledValue
 
@@ -210,11 +211,11 @@ def sample(
             *_,
         ) = self.infection_to_admission_interval_rv(**kwargs)
 
-        latent_hospital_admissions = jnp.convolve(
-            infection_hosp_rate.value * latent_infections.value,
+        latent_hospital_admissions = compute_delay_ascertained_incidence(
+            infection_hosp_rate.value,
+            latent_infections.value,
             infection_to_admission_interval.value,
-            mode="full",
-        )[: latent_infections.value.shape[0]]
+        )
 
         # Applying the day of the week effect. For this we need to:
         # 1. Get the day of the week effect

test/test_incidence_observed_with_delay.py

@@ -0,0 +1,49 @@
+# numpydoc ignore=GL08
+
+import jax.numpy as jnp
+import pytest
+from numpy.testing import assert_array_equal
+
+from pyrenew.convolve import compute_delay_ascertained_incidence
+
+
+@pytest.mark.parametrize(
+    ["obs_rate", "latent_incidence", "delay_interval", "expected_output"],
+    [
+        [
+            jnp.array([1.0]),
+            jnp.array([1.0, 2.0, 3.0]),
+            jnp.array([1.0]),
+            jnp.array([1.0, 2.0, 3.0]),
+        ],
+        [
+            jnp.array([1.0, 0.1, 1.0]),
+            jnp.array([1.0, 2.0, 3.0]),
+            jnp.array([1.0]),
+            jnp.array([1.0, 0.2, 3.0]),
+        ],
+        [
+            jnp.array([1.0]),
+            jnp.array([1.0, 2.0, 3.0]),
+            jnp.array([0.5, 0.5]),
+            jnp.array([1.5, 2.5]),
+        ],
+        [
+            jnp.array([1.0]),
+            jnp.array([0, 2.0, 4.0]),
+            jnp.array([0.25, 0.5, 0.25]),
+            jnp.array([2]),
+        ],
+    ],
+)
+def test(obs_rate, latent_incidence, delay_interval, expected_output):
+    """
+    Tests for helper function to compute
+    incidence observed with a delay
+    """
+    result = compute_delay_ascertained_incidence(
+        obs_rate,
+        latent_incidence,
+        delay_interval,
+    )
+    assert_array_equal(result, expected_output)