Add monthly daly reporting to individual history tracker

src/tlo/methods/healthburden.py

@@ -19,6 +19,7 @@
     get_gbd_causes_not_represented_in_disease_modules,
 )
 from tlo.methods.demography import age_at_date
+from tlo.notify import notifier
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
@@ -614,6 +615,20 @@ def apply(self, population):
         # Multiply 1/12 as these weights are for one month only
         disease_specific_daly_values_this_month = disease_specific_daly_values_this_month * (1 / 12)
 
+        if notifier.has_listeners("healthburden.monthly_daly_report"):
+            # Do not dispatch individuals or causes that have zero dalys reported this month
+            monthly_dalys = disease_specific_daly_values_this_month.copy()
+            monthly_dalys_nonzero = (
+                monthly_dalys.loc[(monthly_dalys != 0).any(axis=1), (monthly_dalys != 0).any(axis=0)]
+                  .to_dict(orient="index"))
+
+            # Store data as dictionary
+            data = {
+                person: {col: val for col, val in cols.items() if val != 0}
+                for person, cols in monthly_dalys_nonzero.items()
+            }
+            notifier.dispatch("healthburden.monthly_daly_report", data=data)
+
         # 4) Summarise the results for this month wrt sex/age/wealth
         # - merge in age/wealth/sex information
         disease_specific_daly_values_this_month = disease_specific_daly_values_this_month.merge(

src/tlo/methods/individual_history_tracker.py

@@ -61,6 +61,7 @@ def initialise_simulation(self, sim):
         notifier.add_listener("hsi_event.pre-run", self.on_event_pre_run)
         notifier.add_listener("hsi_event.post-run", self.on_event_post_run)
         notifier.add_listener("consumables.post-request_consumables", self.on_consumable_request)
+        notifier.add_listener("healthburden.monthly_daly_report", self.on_monthly_daly_report)
 
     def read_parameters(self, resourcefilepath: Optional[Path] = None):
         self.load_parameters_from_dataframe(
@@ -371,6 +372,28 @@ def on_event_post_run(self, data):
         self.consumable_access = {}
         self.cons_call_number_within_event = 0
 
+    def on_monthly_daly_report(self,data):
+        """Upon receiving monthly daly report, convert it to custom EAV format and log"""
+        rows = []
+
+        # This is not a real event, so create custom name and create custom tag associated with it
+        event_name = "monthly_daly_report"
+        event_tag = -1
+
+        for person, dalys in data.items():
+            for cause, value in dalys.items():
+                rows.append({
+                    "entity": person,
+                    "event_name": event_name,
+                    "event_tag": event_tag,
+                    "attribute": cause,
+                    "value": value
+                })
+
+        eav = pd.DataFrame(rows)
+        self.log_eav_dataframe_to_individual_histories(eav)
+        return
+
     def mni_values_differ(self, v1, v2):
 
         if isinstance(v1, list) and isinstance(v2, list):

tests/test_individual_history_tracker.py

@@ -11,6 +11,7 @@
     contraception,
     demography,
     enhanced_lifestyle,
+    healthburden,
     healthseekingbehaviour,
     healthsystem,
     hiv,
@@ -56,6 +57,7 @@ def test_individual_history_tracker(tmpdir, seed):
     sim.register(demography.Demography(),
                  enhanced_lifestyle.Lifestyle(),
                  healthsystem.HealthSystem(),
+                 healthburden.HealthBurden(),
                  individual_history_tracker.IndividualHistoryTracker(),
                  symptommanager.SymptomManager(),
                  healthseekingbehaviour.HealthSeekingBehaviour(),
@@ -79,6 +81,14 @@ def test_individual_history_tracker(tmpdir, seed):
     output_chains = parse_log_file(sim.log_filepath, level=logging.INFO)
     individual_histories = reconstruct_individual_histories(
                             output_chains['tlo.methods.individual_history_tracker']['individual_histories'])
+
+    # Check that monthly daly reporting is included
+    assert (individual_histories['event_name'] == 'monthly_daly_report').sum() > 0
+
+    # Cannot estimate how many monthly reports should be expected, since monthly report is only logged if individual
+    # experienced dalys that month, so check that at or below this max
+    max_monthly_reports = ((end_date.year - start_date.year) * 12 + (end_date.month - start_date.month))*popsize
+    assert (individual_histories['event_name'] == 'monthly_daly_report').sum() <= max_monthly_reports
 
     # Check that we have a "StartOfSimulation" event for every individual in the initial population,
     #   and that this was logged at the start date