updates from paper analyses for HIV, TB and malaria

src/scripts/hiv/projections_jan2023/analysis_full_model.py

@@ -23,8 +23,8 @@
 
 # %% Run the simulation
 start_date = Date(2010, 1, 1)
-end_date = Date(2020, 1, 1)
-popsize = 5000
+end_date = Date(2012, 1, 1)
+popsize = 500
 # scenario = 0
 
 # set up the log config

src/scripts/hiv/projections_jan2023/analysis_logged_deviance.py

@@ -34,8 +34,8 @@
 
 # %% Run the simulation
 start_date = Date(2010, 1, 1)
-end_date = Date(2015, 1, 1)
-popsize = 5000
+end_date = Date(2014, 1, 1)
+popsize = 1000
 
 # scenario = 1
 

src/tlo/methods/bladder_cancer.py

@@ -45,7 +45,7 @@ def __init__(self, name=None, resourcefilepath=None):
 
     INIT_DEPENDENCIES = {'Demography', 'Lifestyle', 'HealthSystem', 'SymptomManager'}
 
-    OPTIONAL_INIT_DEPENDENCIES = {'HealthBurden'}
+    OPTIONAL_INIT_DEPENDENCIES = {'HealthBurden', 'Schisto'}
 
     METADATA = {
         Metadata.DISEASE_MODULE,
@@ -242,17 +242,23 @@ def initialise_population(self, population):
         # check parameters are sensible: probability of having any cancer stage cannot exceed 1.0
         assert sum(p['init_prop_bladder_cancer_stage']) <= 1.0
 
-        lm_init_bc_status_any_stage = LinearModel(
-            LinearModelType.MULTIPLICATIVE,
-            sum(p['init_prop_bladder_cancer_stage']),
+        predictors = [
             Predictor('li_tob').when(True, p['rp_bladder_cancer_tobacco']),
-            # todo: add line when schisto is merged
-            # Predictor('sh_infection_status').when('High-infection', p['rp_bladder_cancer_schisto_h']),
             Predictor('age_years', conditions_are_mutually_exclusive=True)
             .when('.between(30,49)', p['rp_bladder_cancer_age3049'])
             .when('.between(50,69)', p['rp_bladder_cancer_age5069'])
             .when('.between(70,120)', p['rp_bladder_cancer_agege70'])
             .when('.between(0,14)', 0.0)
+        ]
+
+        conditional_predictors = [
+            Predictor('ss_sh_infection_status').when('High-infection', p['rp_bladder_cancer_schisto_h']),
+        ] if "Schisto" in self.sim.modules else []
+
+        lm_init_bc_status_any_stage = LinearModel(
+            LinearModelType.MULTIPLICATIVE,
+            sum(p['init_prop_bladder_cancer_stage']),
+            *(predictors + conditional_predictors)
         )
 
         bc_status_any_stage = lm_init_bc_status_any_stage.predict(df.loc[df.is_alive], self.rng)
@@ -397,20 +403,26 @@ def initialise_simulation(self, sim):
         p = self.parameters
         lm = self.linear_models_for_progession_of_bc_status
 
-        lm['tis_t1'] = LinearModel(
-            LinearModelType.MULTIPLICATIVE,
-            p['r_tis_t1_bladder_cancer_none'],
-            # todo: add in when schisto is in
-            # Predictor('sh_infection_status').when('High-infection', p['rp_bladder_cancer_schisto_h']),
+        predictors = [
             Predictor('age_years', conditions_are_mutually_exclusive=True)
             .when('.between(30,49)', p['rp_bladder_cancer_age3049'])
             .when('.between(50,69)', p['rp_bladder_cancer_age5069'])
             .when('.between(70,120)', p['rp_bladder_cancer_agege70'])
             .when('.between(0,14)', 0.0),
             Predictor('li_tob').when(True, p['rr_tis_t1_bladder_cancer_none_tobacco']),
-            # todo: add in when schisto module in master
-            # Predictor('sh_').when(True, p['rr_tis_t1_bladder_cancer_none_ex_alc']),
-            Predictor('bc_status').when('none', 1.0).otherwise(0.0)
+            # todo:
+            # Predictor('tmp_').when(True, p['rr_tis_t1_bladder_cancer_none_ex_alc']),
+            Predictor('bc_status').when('none', 1.0).otherwise(0.0),
+        ]
+
+        conditional_predictors = [
+            Predictor('ss_sh_infection_status').when('High-infection', p['rp_bladder_cancer_schisto_h']),
+        ] if "Schisto" in self.sim.modules else []
+
+        lm["tis_t1"] = LinearModel(
+            LinearModelType.MULTIPLICATIVE,
+            p['r_tis_t1_bladder_cancer_none'],
+            *(predictors + conditional_predictors)
         )
 
         lm['t2p'] = LinearModel(

src/tlo/methods/cardio_metabolic_disorders.py

@@ -61,7 +61,7 @@ class CardioMetabolicDisorders(Module):
 
     INIT_DEPENDENCIES = {'Demography', 'Lifestyle', 'HealthSystem', 'SymptomManager'}
 
-    OPTIONAL_INIT_DEPENDENCIES = {'HealthBurden'}
+    OPTIONAL_INIT_DEPENDENCIES = {'HealthBurden', 'Hiv'}
 
     ADDITIONAL_DEPENDENCIES = {'Depression'}
 
@@ -517,7 +517,7 @@ def initialise_simulation(self, sim):
         # Hypertension is the only condition for which we assume some community-based testing occurs; build LM based on
         # age / sex
         self.lms_testing['hypertension'] = self.build_linear_model('hypertension', self.parameters[
-                'interval_between_polls'], lm_type='testing')
+            'interval_between_polls'], lm_type='testing')
 
         for event in self.events:
             self.lms_event_onset[event] = self.build_linear_model(event, self.parameters['interval_between_polls'],
@@ -598,9 +598,7 @@ def build_linear_model(self, condition, interval_between_polls, lm_type):
         # LinearModel expects native python types - if it's numpy type, convert it
         baseline_annual_probability = float(baseline_annual_probability)
 
-        linearmodel = LinearModel(
-            LinearModelType.MULTIPLICATIVE,
-            baseline_annual_probability,
+        predictors = [
             Predictor('sex').when('M', p['rr_male']),
             Predictor(
                 'age_years',
@@ -693,6 +691,17 @@ def build_linear_model(self, condition, interval_between_polls, lm_type):
                 'rr_chronic_ischemic_heart_disease_on_medication']),
             Predictor('nc_ever_stroke_on_medication').when(True, p['rr_stroke_on_medication']),
             Predictor('nc_ever_heart_attack_on_medication').when(True, p['rr_heart_attack_on_medication'])
+        ]
+
+        conditional_predictors = [
+            Predictor().when('hv_inf & '
+                             '(hv_art != "on_VL_suppressed")', p['rr_hiv']),
+        ] if "Hiv" in self.sim.modules else []
+
+        linearmodel = LinearModel(
+            LinearModelType.MULTIPLICATIVE,
+            baseline_annual_probability,
+            *(predictors + conditional_predictors)
         )
 
         return linearmodel
@@ -856,6 +865,7 @@ def do_at_generic_first_appt(
 
         # Schedule follow-up HSI *if* there are any conditions to investigate:
         if conditions_to_investigate:
+
             event = HSI_CardioMetabolicDisorders_Investigations(
                 module=self,
                 person_id=patient_id,
@@ -1613,7 +1623,7 @@ def apply(self, person_id, squeeze_factor):
             # NB. With a probability of 1.0, this will keep occurring, and the person will never give up coming back to
             # pick up medication.
             if (m.rng.random_sample() <
-                    m.parameters[f'{self.condition}_hsi'].get('pr_seeking_further_appt_if_drug_not_available')):
+                m.parameters[f'{self.condition}_hsi'].get('pr_seeking_further_appt_if_drug_not_available')):
                 self.sim.modules['HealthSystem'].schedule_hsi_event(
                     hsi_event=self,
                     topen=self.sim.date + pd.DateOffset(days=1),
@@ -1678,7 +1688,7 @@ def apply(self, person_id, squeeze_factor):
             # NB. With a probability of 1.0, this will keep occurring, and the person will never give-up coming back to
             # pick-up medication.
             if (m.rng.random_sample() <
-                    m.parameters[f'{self.condition}_hsi'].get('pr_seeking_further_appt_if_drug_not_available')):
+                m.parameters[f'{self.condition}_hsi'].get('pr_seeking_further_appt_if_drug_not_available')):
                 self.sim.modules['HealthSystem'].schedule_hsi_event(
                     hsi_event=self,
                     topen=self.sim.date + pd.DateOffset(days=1),