Maternal/Newborn health cohort model

src/scripts/maternal_perinatal_analyses/cohort_analysis/cohort_interventions_scenario.py

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+import numpy as np
+import pandas as pd
+
+from pathlib import Path
+
+from tlo import Date, logging
+from tlo.methods import mnh_cohort_module
+from tlo.methods.fullmodel import fullmodel
+from tlo.scenario import BaseScenario
+
+
+class BaselineScenario(BaseScenario):
+    def __init__(self):
+        super().__init__()
+        self.seed = 537184
+        self.start_date = Date(2024, 1, 1)
+        self.end_date = Date(2025, 1, 2)
+        self.pop_size = 5000
+        self.number_of_draws = 3
+        self.runs_per_draw = 10
+
+    def log_configuration(self):
+        return {
+            'filename': 'block_intervention_test', 'directory': './outputs',
+            "custom_levels": {
+                "*": logging.WARNING,
+                "tlo.methods.demography": logging.INFO,
+                "tlo.methods.demography.detail": logging.INFO,
+                "tlo.methods.contraception": logging.INFO,
+                "tlo.methods.healthsystem.summary": logging.INFO,
+                "tlo.methods.healthburden": logging.INFO,
+                "tlo.methods.labour": logging.INFO,
+                "tlo.methods.labour.detail": logging.INFO,
+                "tlo.methods.newborn_outcomes": logging.INFO,
+                "tlo.methods.care_of_women_during_pregnancy": logging.INFO,
+                "tlo.methods.pregnancy_supervisor": logging.INFO,
+                "tlo.methods.postnatal_supervisor": logging.INFO,
+            }
+        }
+
+    def modules(self):
+        return [*fullmodel(resourcefilepath=self.resources,
+                           module_kwargs={'Schisto': {'mda_execute': False}}),
+                 mnh_cohort_module.MaternalNewbornHealthCohort(resourcefilepath=self.resources)]
+
+    def draw_parameters(self, draw_number, rng):
+        interventions_for_analysis = ['blood_transfusion', 'pph_treatment_uterotonics', 'sepsis_treatment']
+
+        return {'PregnancySupervisor': {
+                'analysis_year': 2024,
+                'interventions_analysis': True,
+                'interventions_under_analysis':[interventions_for_analysis[draw_number-1]],
+                'intervention_analysis_availability': 0.0}}
+
+
+if __name__ == '__main__':
+    from tlo.cli import scenario_run
+    scenario_run([__file__])

src/scripts/maternal_perinatal_analyses/cohort_analysis/dummy_cohort_azure_calib.py

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+import os
+
+import pandas as pd
+
+from tlo.analysis.utils import extract_results, get_scenario_outputs, summarize
+
+outputspath = './outputs/[email protected]/'
+scenario_filename = 'cohort_test-2024-10-09T130546Z'
+# scenario_filename2 = 'cohort_test-2024-10-15T122825Z'
+scenario_filename2 = 'cohort_test-2024-10-16T071357Z'
+
+results_folder_old = get_scenario_outputs(scenario_filename, outputspath)[-1]
+results_folder_new = get_scenario_outputs(scenario_filename2, outputspath)[-1]
+
+def get_data_frames(key, results_folder):
+    def sort_df(_df):
+        _x = _df.drop(columns=['date'], inplace=False)
+        return _x.iloc[0]
+
+    results_df = summarize (extract_results(
+                results_folder,
+                module="tlo.methods.pregnancy_supervisor",
+                key=key,
+                custom_generate_series=sort_df,
+                do_scaling=False
+            ))
+
+    return results_df
+
+results_old = {k:get_data_frames(k, results_folder_old) for k in
+               ['mat_comp_incidence', 'nb_comp_incidence', 'deaths_and_stillbirths','service_coverage',
+                'yearly_mnh_counter_dict']}
+
+results_new = {k:get_data_frames(k, results_folder_new) for k in
+               ['mat_comp_incidence', 'nb_comp_incidence', 'deaths_and_stillbirths', 'service_coverage',
+                'yearly_mnh_counter_dict']}
+
+import matplotlib.pyplot as plt
+import numpy as np
+# Sample data
+mmr_data = {
+    'int_1': [(235, 250, 265), (335, 350, 365)],
+    'int_2': [(170, 195, 200), (290, 305, 320)],
+    'int_3': [(280, 295, 310), (295 ,310, 325)],
+    'int_4': [(165, 180, 195), (385, 400, 415)]
+}
+# Plotting
+fig, ax = plt.subplots()
+for key, intervals in mmr_data.items():
+    for idx, (lower, mean, upper) in enumerate(intervals):
+        x = np.arange(len(mmr_data)) * len(intervals) + idx
+        ax.plot(x, mean, 'o', label=f'{key}' if idx == 0 else "")
+        ax.fill_between([x, x], [lower, lower], [upper, upper], alpha=0.2)
+ax.set_xticks(np.arange(len(mmr_data)) * len(intervals) + 0.5)
+ax.set_xticklabels(mmr_data.keys())
+plt.legend()
+plt.show()

src/scripts/maternal_perinatal_analyses/cohort_analysis/local_run_cohort_calibration.py

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+import os
+from pathlib import Path
+
+import pandas as pd
+
+from tlo import Date, Simulation, logging
+from tlo.methods import mnh_cohort_module
+from tlo.methods.fullmodel import fullmodel
+from tlo.analysis.utils import parse_log_file
+
+
+resourcefilepath = Path('./resources')
+outputpath = Path("./outputs/cohort_testing")  # folder for convenience of storing outputs
+population_size = 2000
+
+sim = Simulation(start_date=Date(2024, 1, 1),
+                 seed=456,
+                 log_config={"filename": "log_cohort_calibration",
+                             "custom_levels": {"*": logging.DEBUG},
+                             "directory": outputpath})
+
+sim.register(*fullmodel(resourcefilepath=resourcefilepath),
+             mnh_cohort_module.MaternalNewbornHealthCohort(resourcefilepath=resourcefilepath))
+
+sim.make_initial_population(n=population_size)
+sim.simulate(end_date=Date(2025, 1, 1))
+
+output = parse_log_file(sim.log_filepath)
+
+# output = parse_log_file(
+#              '/Users/j_collins/PycharmProjects/TLOmodel/outputs/log_cohort_calibration__2024-10-04T101535.log')
+
+# Make output dataframe
+results = pd.DataFrame(columns=['model', 'data', 'source'],
+                       index= ['deaths',
+                               'MMR',
+                               'DALYs',
+                               'twins',
+                               'ectopic',
+                               'abortion',
+                               'miscarriage',
+                               'syphilis',
+                               'anaemia_an',
+                               'anaemia_pn'
+                               'gdm',
+                               'PROM',
+                               'pre_eclampsia',
+                               'gest-htn',
+                               'severe_gest-htn',
+                               'severe pre-eclampsia',
+                               'eclampsia',
+                               'praevia',
+                               'abruption',
+                               'aph',
+                               'OL',
+                               'UR',
+                               'sepsis',
+                               'PPH'])
+
+# total_pregnancies = population_size
+total_pregnancies = 2000
+total_births = len(output['tlo.methods.demography']['on_birth'])
+prop_live_births = (total_births/total_pregnancies) * 100
+
+# Mortality/DALY
+deaths_df = output['tlo.methods.demography']['death']
+prop_deaths_df = output['tlo.methods.demography.detail']['properties_of_deceased_persons']
+
+dir_mat_deaths = deaths_df.loc[(deaths_df['label'] == 'Maternal Disorders')]
+init_indir_mat_deaths = prop_deaths_df.loc[(prop_deaths_df['is_pregnant'] | prop_deaths_df['la_is_postpartum']) &
+                                  (prop_deaths_df['cause_of_death'].str.contains('Malaria|Suicide|ever_stroke|diabetes|'
+                                                                     'chronic_ischemic_hd|ever_heart_attack|'
+                                                                     'chronic_kidney_disease') |
+                                   (prop_deaths_df['cause_of_death'] == 'TB'))]
+
+hiv_mat_deaths =  prop_deaths_df.loc[(prop_deaths_df['is_pregnant'] | prop_deaths_df['la_is_postpartum']) &
+                              (prop_deaths_df['cause_of_death'].str.contains('AIDS_non_TB|AIDS_TB'))]
+
+indir_mat_deaths = len(init_indir_mat_deaths) + (len(hiv_mat_deaths) * 0.3)
+total_deaths = len(dir_mat_deaths) + indir_mat_deaths
+
+# TOTAL_DEATHS
+results.at['deaths', 'model'] = total_deaths
+results.at['MMR', 'model'] = (total_deaths / total_births) * 100_000
+results.at['DALYs', 'model'] = output['tlo.methods.healthburden']['dalys_stacked']['Maternal Disorders'].sum()
+
+# Maternal conditions
+an_comps = output['tlo.methods.pregnancy_supervisor']['maternal_complication']
+la_comps = output['tlo.methods.labour']['maternal_complication']
+pn_comps = output['tlo.methods.postnatal_supervisor']['maternal_complication']
+
+twin_births = len(output['tlo.methods.newborn_outcomes']['twin_birth'])
+
+total_completed_pregnancies = (len(an_comps.loc[an_comps['type'] == 'ectopic_unruptured']) +
+                               len(an_comps.loc[an_comps['type'] == 'induced_abortion']) +
+                               len(an_comps.loc[an_comps['type'] == 'spontaneous_abortion']) +
+                               (total_births - twin_births) +
+                               len(output['tlo.methods.pregnancy_supervisor']['antenatal_stillbirth']) +
+                               len(output['tlo.methods.labour']['intrapartum_stillbirth']))
+
+print(total_completed_pregnancies)  # this value may be less than the starting population size due to antenatal
+# maternal deaths
+
+# Twins (todo)
+
+# Ectopic
+results.at['ectopic', 'model'] = (len(an_comps.loc[an_comps['type'] == 'ectopic_unruptured']) / total_pregnancies) * 1000
+results.at['ectopic', 'data'] = 10.0
+results.at['ectopic', 'source'] = 'Panelli et al.'
+
+# Abortion
+
+
+# Miscarriage
+
+# Health system

src/scripts/maternal_perinatal_analyses/scenario_files/full_model_long_run_cohort.py

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+from tlo import Date, logging
+from tlo.methods.fullmodel import fullmodel
+
+from tlo.scenario import BaseScenario
+
+
+class FullModelRunForCohort(BaseScenario):
+    def __init__(self):
+        super().__init__()
+        self.seed = 537184
+        self.start_date = Date(2010, 1, 1)
+        self.end_date = Date(2025, 1, 1)
+        self.pop_size = 200_000
+        self.number_of_draws = 1
+        self.runs_per_draw = 1
+
+    def log_configuration(self):
+        return {
+            'filename': 'fullmodel_200k_cohort', 'directory': './outputs',
+            "custom_levels": {
+                "*": logging.WARNING,
+                "tlo.methods.contraception": logging.DEBUG,
+            }
+        }
+
+    def modules(self):
+        return fullmodel(resourcefilepath=self.resources)
+
+    def draw_parameters(self, draw_number, rng):
+        return {}
+
+
+if __name__ == '__main__':
+    from tlo.cli import scenario_run
+    scenario_run([__file__])