Overwrite any changes to hiv and tb file

Author: marghe-molaro

src/tlo/methods/hiv.py

@@ -1682,37 +1682,6 @@ def do_at_generic_first_appt(
 #   Main Polling Event
 # ---------------------------------------------------------------------------
 
-class HivPollingEventForDataGeneration(RegularEvent, PopulationScopeEventMixin):
-    """ The HIV Polling Events for Data Generation
-    * Ensures that 
-    """
-
-    def __init__(self, module):
-        super().__init__(
-            module, frequency=DateOffset(years=120)
-        )  # repeats every 12 months, but this can be changed
-
-    def apply(self, population):
-    
-        df = population.props
-        
-        # Make everyone who is alive and not infected (no-one should be) susceptible
-        susc_idx = df.loc[
-            df.is_alive
-            & ~df.hv_inf
-            ].index
-            
-        n_susceptible = len(susc_idx)
-        print("Number of individuals susceptible", n_susceptible)
-        # Schedule the date of infection for each new infection:
-        for i in susc_idx:
-            date_of_infection = self.sim.date + pd.DateOffset(
-                # Ensure that individual will be infected before end of sim
-                days=self.module.rng.randint(0, 365*(int(self.sim.end_date.year - self.sim.date.year)+1))
-            )
-            self.sim.schedule_event(
-                HivInfectionEvent(self.module, i), date_of_infection
-            )
 
 class HivRegularPollingEvent(RegularEvent, PopulationScopeEventMixin):
     """ The HIV Regular Polling Events
@@ -1734,7 +1703,6 @@ def apply(self, population):
         fraction_of_year_between_polls = self.frequency.months / 12
         beta = p["beta"] * fraction_of_year_between_polls
 
-        
         # ----------------------------------- HORIZONTAL TRANSMISSION -----------------------------------
         def horizontal_transmission(to_sex, from_sex):
             # Count current number of alive 15-80 year-olds at risk of transmission
@@ -1810,7 +1778,6 @@ def horizontal_transmission(to_sex, from_sex):
                         HivInfectionEvent(self.module, idx), date_of_infection
                     )
 
-
         # ----------------------------------- SPONTANEOUS TESTING -----------------------------------
         def spontaneous_testing(current_year):
 
@@ -1935,8 +1902,6 @@ def vmmc_for_child():
         vmmc_for_child()
 
 
-
-
 # ---------------------------------------------------------------------------
 #   Natural History Events
 # ---------------------------------------------------------------------------

src/tlo/methods/tb.py

@@ -864,31 +864,29 @@ def initialise_population(self, population):
         df["tb_on_ipt"] = False
         df["tb_date_ipt"] = pd.NaT
 
-
         # # ------------------ infection status ------------------ #
-        if self.sim.generate_event_chains is False or self.sim.generate_event_chains is None:
-            # WHO estimates of active TB for 2010 to get infected initial population
-            # don't need to scale or include treated proportion as no-one on treatment yet
-            inc_estimates = p["who_incidence_estimates"]
-            incidence_year = (inc_estimates.loc[
-                (inc_estimates.year == self.sim.date.year), "incidence_per_100k"
-            ].values[0]) / 100_000
-
-            incidence_year = incidence_year * p["scaling_factor_WHO"]
-
-            self.assign_active_tb(
-                population,
-                strain="ds",
-                incidence=incidence_year)
-
-            self.assign_active_tb(
-                population,
-                strain="mdr",
-                incidence=incidence_year * p['prop_mdr2010'])
-
-            self.send_for_screening_general(
-                population
-            )  # send some baseline population for screening
+        # WHO estimates of active TB for 2010 to get infected initial population
+        # don't need to scale or include treated proportion as no-one on treatment yet
+        inc_estimates = p["who_incidence_estimates"]
+        incidence_year = (inc_estimates.loc[
+            (inc_estimates.year == self.sim.date.year), "incidence_per_100k"
+        ].values[0]) / 100_000
+
+        incidence_year = incidence_year * p["scaling_factor_WHO"]
+
+        self.assign_active_tb(
+            population,
+            strain="ds",
+            incidence=incidence_year)
+
+        self.assign_active_tb(
+            population,
+            strain="mdr",
+            incidence=incidence_year * p['prop_mdr2010'])
+
+        self.send_for_screening_general(
+            population
+        )  # send some baseline population for screening
 
     def initialise_simulation(self, sim):
         """
@@ -901,10 +899,8 @@ def initialise_simulation(self, sim):
         sim.schedule_event(TbActiveEvent(self), sim.date)
         sim.schedule_event(TbRegularEvents(self), sim.date)
         sim.schedule_event(TbSelfCureEvent(self), sim.date)
-
         sim.schedule_event(TbActiveCasePoll(self), sim.date + DateOffset(years=1))
 
-
         # 2) log at the end of the year
         # Optional: Schedule the scale-up of programs
         if self.parameters["type_of_scaleup"] != 'none':
@@ -1406,53 +1402,6 @@ def is_subset(col_for_set, col_for_subset):
 # #   TB infection event
 # # ---------------------------------------------------------------------------
 
-class TbActiveCasePollGenerateData(RegularEvent, PopulationScopeEventMixin):
-    """The Tb Regular Poll Event for Data Generation for assigning active infections
-    * selects everyone to develop an active infection and schedules onset of active tb
-    sometime during the simulation
-    """
-
-    def __init__(self, module):
-        super().__init__(module, frequency=DateOffset(years=120))
-
-    def apply(self, population):
-
-        df = population.props
-        now = self.sim.date
-        rng = self.module.rng
-        # Make everyone who is alive and not infected (no-one should be) susceptible
-        susc_idx = df.loc[
-            df.is_alive
-            & (df.tb_inf != "active")
-            ].index
-            
-        len(susc_idx)
-        
-        middle_index = len(susc_idx) // 2
-
-        # Will equally split two strains among the population
-        list_ds = susc_idx[:middle_index]
-        list_mdr = susc_idx[middle_index:]
-    
-        # schedule onset of active tb. This will be equivalent to the "Onset", so it
-        # doesn't matter how long after we have decided which infection this is.
-        for person_id in list_ds:
-            date_progression = now + pd.DateOffset(
-                # At some point during their lifetime, this person will develop TB
-                days=self.module.rng.randint(0, 365*(int(self.sim.end_date.year - self.sim.date.year)+1))
-            )
-            # set date of active tb - properties will be updated at TbActiveEvent poll daily
-            df.at[person_id, "tb_scheduled_date_active"] = date_progression
-            df.at[person_id, "tb_strain"] = "ds"
-            
-        for person_id in list_mdr:
-            date_progression = now + pd.DateOffset(
-                days=rng.randint(0, 365*int(self.sim.end_date.year - self.sim.start_date.year + 1))
-            )
-            # set date of active tb - properties will be updated at TbActiveEvent poll daily
-            df.at[person_id, "tb_scheduled_date_active"] = date_progression
-            df.at[person_id, "tb_strain"] = "mdr"
-            
 
 class TbActiveCasePoll(RegularEvent, PopulationScopeEventMixin):
     """The Tb Regular Poll Event for assigning active infections
@@ -1527,6 +1476,7 @@ def apply(self, population):
         self.module.update_parameters_for_program_scaleup()
         # note also culture test used in target/max scale-up in place of clinical dx
 
+
 class TbActiveEvent(RegularEvent, PopulationScopeEventMixin):
     """
     * check for those with dates of active tb onset within last time-period