Generate chain of events for individuals

src/scripts/analysis_data_generation/postprocess_events_chain.py

@@ -0,0 +1,156 @@
+import pandas as pd
+from dateutil.relativedelta import relativedelta
+
+# Remove from every individual's event chain all events that were fired after death
+def cut_off_events_after_death(df):
+
+    events_chain = df.groupby('person_ID')
+
+    filtered_data = pd.DataFrame()
+
+    for name, group in events_chain:
+
+        # Find the first non-NaN 'date_of_death' and its index
+        first_non_nan_index = group['date_of_death'].first_valid_index()
+
+        if first_non_nan_index is not None:
+            # Filter out all rows after the first non-NaN index
+            filtered_group = group.loc[:first_non_nan_index]  # Keep rows up to and including the first valid index
+            filtered_data = pd.concat([filtered_data, filtered_group])
+        else:
+            # If there are no non-NaN values, keep the original group
+            filtered_data = pd.concat([filtered_data, group])
+
+    return filtered_data
+
+# Load into DataFrame
+def load_csv_to_dataframe(file_path):
+    try:
+        # Load raw chains into df
+        df = pd.read_csv(file_path)
+        print("Raw event chains loaded successfully!")
+        return df
+    except FileNotFoundError:
+        print(f"Error: The file '{file_path}' was not found.")
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+file_path = 'output.csv'  # Replace with the path to your CSV file
+
+output = load_csv_to_dataframe(file_path)
+
+# Some of the dates appeared not to be in datetime format. Correct here.
+output['date_of_death'] = pd.to_datetime(output['date_of_death'], errors='coerce')
+output['date_of_birth'] = pd.to_datetime(output['date_of_birth'], errors='coerce')
+if 'hv_date_inf' in output.columns:
+    output['hv_date_inf'] = pd.to_datetime(output['hv_date_inf'], errors='coerce')
+
+
+date_start = pd.to_datetime('2010-01-01')
+if 'Other' in output['cause_of_death'].values:
+    print("ERROR: 'Other' was included in sim as possible cause of death")
+    exit(-1)
+
+# Choose which columns in individual properties to visualise
+columns_to_print =['event','is_alive','hv_inf', 'hv_art','tb_inf', 'tb_date_active', 'event_date', 'when']
+#columns_to_print =['person_ID', 'date_of_birth', 'date_of_death', 'cause_of_death','hv_date_inf', 'hv_art','tb_inf', 'tb_date_active', 'event date', 'event']
+
+# When checking which individuals led to *any* changes in individual properties, exclude these columns from comparison
+columns_to_exclude_in_comparison = ['when', 'event', 'event_date', 'age_exact_years', 'age_years', 'age_days', 'age_range', 'level', 'appt_footprint']
+
+# If considering epidemiology consistent with sim, add check here.
+check_ages_of_those_HIV_inf = False
+if check_ages_of_those_HIV_inf:
+    for index, row in output.iterrows():
+        if pd.isna(row['hv_date_inf']):
+            continue  # Skip this iteration
+        diff = relativedelta(output.loc[index, 'hv_date_inf'],output.loc[index, 'date_of_birth'])
+        if diff.years > 1 and diff.years<15:
+            print("Person contracted HIV infection at age younger than 15", diff)
+
+# Remove events after death
+filtered_data = cut_off_events_after_death(output)
+
+print_raw_events = True # Print raw chain of events for each individual
+print_selected_changes = False
+print_all_changes = True
+person_ID_of_interest = 494
+
+pd.set_option('display.max_rows', None)
+
+for name, group in filtered_data.groupby('person_ID'):
+    list_of_dob = group['date_of_birth']
+
+    # Select individuals based on when they were born
+    if list_of_dob.iloc[0].year<2010:
+
+        # Check that immutable properties are fixed for this individual, i.e. that events were collated properly:
+        all_identical_dob = group['date_of_birth'].nunique() == 1
+        all_identical_sex = group['sex'].nunique() == 1
+        if all_identical_dob is False or all_identical_sex is False:
+            print("Immutable properties are changing! This is not chain for single individual")
+            print(group)
+            exit(-1)
+
+        print("----------------------------------------------------------------------")
+        print("person_ID ", group['person_ID'].iloc[0], "d.o.b ", group['date_of_birth'].iloc[0])
+        print("Number of events for this individual ", group['person_ID'].iloc[0], "is :", len(group)/2) # Divide by 2 before printing Before/After for each event
+        number_of_events =len(group)/2
+        number_of_changes=0
+        if print_raw_events:
+            print(group)
+
+        if print_all_changes:
+            # Check each row
+            comparison = group.drop(columns=columns_to_exclude_in_comparison).fillna(-99999).ne(group.drop(columns=columns_to_exclude_in_comparison).shift().fillna(-99999))
+
+            # Iterate over rows where any column has changed
+            for idx, row_changed in comparison.iloc[1:].iterrows():
+                if row_changed.any():  # Check if any column changed in this row
+                    number_of_changes+=1
+                    changed_columns = row_changed[row_changed].index.tolist()  # Get the columns where changes occurred
+                    print(f"Row {idx} - Changes detected in columns: {changed_columns}")
+                    columns_output = ['event', 'event_date', 'appt_footprint', 'level'] + changed_columns
+                    print(group.loc[idx, columns_output])  # Print only the changed columns
+                    if group.loc[idx, 'when'] == 'Before':
+                        print('-----> THIS CHANGE OCCURRED BEFORE EVENT!')
+                    #print(group.loc[idx,columns_to_print])
+                    print()  # For better readability
+            print("Number of changes is ", number_of_changes, "out of ", number_of_events, " events")
+
+        if print_selected_changes:
+            tb_inf_condition = (
+                ((group['tb_inf'].shift(1) == 'uninfected') & (group['tb_inf'] == 'active')) |
+                ((group['tb_inf'].shift(1) == 'latent') & (group['tb_inf'] == 'active')) |
+                ((group['tb_inf'].shift(1) == 'active') & (group['tb_inf'] == 'latent')) |
+                ((group['hv_inf'].shift(1) is False) & (group['hv_inf'] is True)) |
+                ((group['hv_art'].shift(1) == 'not') & (group['hv_art'] == 'on_not_VL_suppressed')) |
+                ((group['hv_art'].shift(1) == 'not') & (group['hv_art'] == 'on_VL_suppressed')) |
+                ((group['hv_art'].shift(1) == 'on_VL_suppressed') & (group['hv_art'] == 'on_not_VL_suppressed')) |
+                ((group['hv_art'].shift(1) == 'on_VL_suppressed') & (group['hv_art'] == 'not')) |
+                ((group['hv_art'].shift(1) == 'on_not_VL_suppressed') & (group['hv_art'] == 'on_VL_suppressed')) |
+                ((group['hv_art'].shift(1) == 'on_not_VL_suppressed') & (group['hv_art'] == 'not'))
+            )
+
+            alive_condition = (
+                (group['is_alive'].shift(1) is True) & (group['is_alive'] is False)
+            )
+            # Combine conditions for rows of interest
+            transition_condition = tb_inf_condition | alive_condition
+
+            if list_of_dob.iloc[0].year >= 2010:
+                print("DETECTED OF INTEREST")
+                print(group[group['event'] == 'Birth'][columns_to_print])
+
+            # Filter the DataFrame based on the condition
+            filtered_transitions = group[transition_condition]
+            if not filtered_transitions.empty:
+                if list_of_dob.iloc[0].year < 2010:
+                    print("DETECTED OF INTEREST")
+                print(filtered_transitions[columns_to_print])
+
+
+print("Number of individuals simulated ", filtered_data.groupby('person_ID').ngroups)
+
+
+

src/scripts/analysis_data_generation/scenario_generate_chains.py

@@ -0,0 +1,152 @@
+"""This Scenario file run the model to generate event chans
+
+Run on the batch system using:
+```
+tlo batch-submit 
+    src/scripts/analysis_data_generation/scenario_generate_chains.py
+```
+
+or locally using:
+```
+    tlo scenario-run src/scripts/analysis_data_generation/scenario_generate_chains.py
+```
+
+"""
+from pathlib import Path
+from typing import Dict
+
+import pandas as pd
+
+from tlo import Date, logging
+from tlo.analysis.utils import get_parameters_for_status_quo, mix_scenarios
+from tlo.methods.fullmodel import fullmodel
+from tlo.methods.scenario_switcher import ImprovedHealthSystemAndCareSeekingScenarioSwitcher
+from tlo.scenario import BaseScenario
+from tlo.methods import (
+    alri,
+    cardio_metabolic_disorders,
+    care_of_women_during_pregnancy,
+    contraception,
+    demography,
+    depression,
+    diarrhoea,
+    enhanced_lifestyle,
+    epi,
+    healthburden,
+    healthseekingbehaviour,
+    healthsystem,
+    hiv,
+    rti,
+    labour,
+    malaria,
+    newborn_outcomes,
+    postnatal_supervisor,
+    pregnancy_supervisor,
+    stunting,
+    symptommanager,
+    tb,
+    wasting,
+)
+
+class GenerateDataChains(BaseScenario):
+    def __init__(self):
+        super().__init__()
+        self.seed = 0
+        self.start_date = Date(2010, 1, 1)
+        self.end_date = self.start_date + pd.DateOffset(months=13)
+        self.pop_size = 1000
+        self._scenarios = self._get_scenarios()
+        self.number_of_draws = len(self._scenarios)
+        self.runs_per_draw = 50
+        self.generate_event_chains = True
+
+    def log_configuration(self):
+        return {
+            'filename': 'generate_event_chains',
+            'directory': Path('./outputs'),  # <- (specified only for local running)
+            'custom_levels': {
+                '*': logging.WARNING,
+                'tlo.methods.demography': logging.INFO,
+                'tlo.methods.events': logging.INFO,
+                'tlo.methods.demography.detail': logging.WARNING,
+                'tlo.methods.healthburden': logging.INFO,
+                'tlo.methods.healthsystem.summary': logging.INFO,
+            }
+        }
+
+    def modules(self):
+        # MODIFY
+        # Here instead of running full module
+        return [demography.Demography(resourcefilepath=self.resources),
+                enhanced_lifestyle.Lifestyle(resourcefilepath=self.resources),
+                healthburden.HealthBurden(resourcefilepath=self.resources),
+                symptommanager.SymptomManager(resourcefilepath=self.resources, spurious_symptoms=False),
+                rti.RTI(resourcefilepath=self.resources),
+                healthseekingbehaviour.HealthSeekingBehaviour(resourcefilepath=self.resources),
+                #simplified_births.SimplifiedBirths(resourcefilepath=resourcefilepath),
+                healthsystem.HealthSystem(resourcefilepath=self.resources,
+                                          mode_appt_constraints=1,
+                                          cons_availability='all')]
+
+       # return (
+       #     fullmodel(resourcefilepath=self.resources)
+       #     + [ImprovedHealthSystemAndCareSeekingScenarioSwitcher(resourcefilepath=self.resources)]
+       # )
+
+    def draw_parameters(self, draw_number, rng):
+        if draw_number < self.number_of_draws:
+            return list(self._scenarios.values())[draw_number]
+        else:
+            return
+
+    # case 1: gfHE = -0.030, factor = 1.01074
+    # case 2: gfHE = -0.020, factor = 1.02116
+    # case 3: gfHE = -0.015, factor = 1.02637
+    # case 4: gfHE =  0.015, factor = 1.05763
+    # case 5: gfHE =  0.020, factor = 1.06284
+    # case 6: gfHE =  0.030, factor = 1.07326
+
+    def _get_scenarios(self) -> Dict[str, Dict]:
+        """Return the Dict with values for the parameters that are changed, keyed by a name for the scenario.
+        """
+
+        self.YEAR_OF_CHANGE = 2019
+
+        return {
+
+            # =========== STATUS QUO ============
+            "Baseline":
+                mix_scenarios(
+                    self._baseline(),
+                    {
+                     "HealthSystem": {
+                        "yearly_HR_scaling_mode": "no_scaling",
+                      },
+                    }
+                ),
+
+        }
+
+    def _baseline(self) -> Dict:
+        """Return the Dict with values for the parameter changes that define the baseline scenario. """
+        return mix_scenarios(
+            get_parameters_for_status_quo(),
+            {
+                "HealthSystem": {
+                    "mode_appt_constraints": 1,                 # <-- Mode 1 prior to change to preserve calibration
+                    "mode_appt_constraints_postSwitch": 2,      # <-- Mode 2 post-change to show effects of HRH
+                    "year_mode_switch": self.YEAR_OF_CHANGE,
+                    "scale_to_effective_capabilities": True,
+                    "policy_name": "Naive",
+                    "tclose_overwrite": 1,
+                    "tclose_days_offset_overwrite": 7,
+                    "use_funded_or_actual_staffing": "actual",
+                    "cons_availability": "default",
+                }
+            },
+        )
+
+if __name__ == '__main__':
+    from tlo.cli import scenario_run
+
+    scenario_run([__file__])