Merge pull request #10 from ihmeuw/update_artifact_scaled_pop

Update artifact to include scaling factor for population

src/vivarium_gates_mncnh/constants/data_keys.py

@@ -15,6 +15,7 @@ class __Population(NamedTuple):
     AGE_BINS: str = "population.age_bins"
     DEMOGRAPHY: str = "population.demographic_dimensions"
     TMRLE: str = "population.theoretical_minimum_risk_life_expectancy"
+    SCALING_FACTOR: str = "population.scaling_factor"
 
     @property
     def name(self):

src/vivarium_gates_mncnh/data/loader.py

@@ -54,6 +54,7 @@ def get_data(
         data_keys.POPULATION.AGE_BINS: load_age_bins,
         data_keys.POPULATION.DEMOGRAPHY: load_demographic_dimensions,
         data_keys.POPULATION.TMRLE: load_theoretical_minimum_risk_life_expectancy,
+        data_keys.POPULATION.SCALING_FACTOR: load_scaling_factor,
         # data_keys.POPULATION.ACMR: load_standard_data,
         # TODO - add appropriate mappings
         data_keys.PREGNANCY.ASFR: load_asfr,
@@ -170,6 +171,8 @@ def _load_em_from_meid(location, meid, measure):
 
 
 # TODO - add project-specific data functions here
+
+
 def load_asfr(
     key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
 ) -> pd.DataFrame:
@@ -210,6 +213,34 @@ def load_raw_incidence_data(
     return vi_utils.sort_hierarchical_data(data).droplevel("location")
 
 
+def load_scaling_factor(
+    key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
+) -> pd.DataFrame:
+
+    incidence_c995 = get_data(
+        data_keys.PREGNANCY.RAW_INCIDENCE_RATE_MISCARRIAGE, location, years
+    ).reset_index()
+    incidence_c374 = get_data(
+        data_keys.PREGNANCY.RAW_INCIDENCE_RATE_ECTOPIC, location, years
+    ).reset_index()
+    asfr = get_data(data_keys.PREGNANCY.ASFR, location).reset_index()
+    sbr = get_data(data_keys.PREGNANCY.SBR, location).reset_index()
+
+    asfr = asfr.set_index(metadata.ARTIFACT_INDEX_COLUMNS)
+    incidence_c995 = incidence_c995.set_index(metadata.ARTIFACT_INDEX_COLUMNS)
+    incidence_c374 = incidence_c374.set_index(metadata.ARTIFACT_INDEX_COLUMNS)
+    sbr = (
+        sbr.set_index("year_start")
+        .drop(columns=["year_end"])
+        .reindex(asfr.index, level="year_start")
+    )
+
+    # Calculate pregnancy incidence
+    preg_inc = asfr + asfr.multiply(sbr["value"], axis=0) + incidence_c995 + incidence_c374
+
+    return preg_inc
+
+
 def load_lbwsg_exposure(
     key: str, location: str, years: Optional[Union[int, str, list[int]]] = None
 ) -> pd.DataFrame:

src/vivarium_gates_mncnh/model_specifications/branches/scenarios.yaml

@@ -1,5 +1,5 @@
 input_draw_count: 10
-random_seed_count: 5
+random_seed_count: 10
 
 branches:
 # TODO change to name of intervention

src/vivarium_gates_mncnh/model_specifications/model_spec.yaml

@@ -7,7 +7,7 @@ plugins:
 components:
     vivarium_public_health:
         population:
-            - BasePopulation()
+            - ScaledPopulation("population.scaling_factor")
 
     vivarium_gates_mncnh:
         components:
@@ -20,7 +20,7 @@ components:
 configuration:
     input_data:
         input_draw_number: 0
-        artifact_path: "/mnt/team/simulation_science/pub/models/vivarium_gates_mncnh/artifacts/anc/ethiopia.hdf"
+        artifact_path: "/mnt/team/simulation_science/pub/models/vivarium_gates_mncnh/artifacts/anc/pakistan.hdf"
     interpolation:
         order: 0
         extrapolate: True