Add default observation rate to compute_delay_ascertained_incidence

pyrenew/convolve.py

@@ -11,6 +11,7 @@
 :py:func:`jax.lax.scan` with an
 appropriate array to scan along.
 """
+
 from __future__ import annotations
 
 from typing import Callable
@@ -166,9 +167,9 @@ def _new_scanner(
 
 
 def compute_delay_ascertained_incidence(
-    p_observed_given_incident: ArrayLike,
     latent_incidence: ArrayLike,
     delay_incidence_to_observation_pmf: ArrayLike,
+    p_observed_given_incident: ArrayLike = 1,
 ) -> ArrayLike:
     """
     Computes incidences observed according

pyrenew/latent/hospitaladmissions.py

@@ -211,9 +211,9 @@ def sample(
         ) = self.infection_to_admission_interval_rv(**kwargs)
 
         latent_hospital_admissions = compute_delay_ascertained_incidence(
-            infection_hosp_rate.value,
             latent_infections.value,
             infection_to_admission_interval.value,
+            infection_hosp_rate.value,
         )
 
         # Applying the day of the week effect. For this we need to:

test/test_incidence_observed_with_delay.py

@@ -34,6 +34,12 @@
             jnp.array([0.25, 0.5, 0.25]),
             jnp.array([2]),
         ],
+        [
+            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):
@@ -42,8 +48,19 @@ def test(obs_rate, latent_incidence, delay_interval, expected_output):
     incidence observed with a delay
     """
     result = compute_delay_ascertained_incidence(
-        obs_rate,
         latent_incidence,
         delay_interval,
+        obs_rate,
     )
     assert_array_equal(result, expected_output)
+
+
+def test_default_obs_rate():
+    """
+    Compute incidence observed with a delay and default observation rate
+    """
+    result = compute_delay_ascertained_incidence(
+        jnp.array([1.0, 2.0, 3.0]),
+        jnp.array([1.0]),
+    )
+    assert_array_equal(result, jnp.array([1.0, 2.0, 3.0]))