Maternal/Newborn health cohort model

src/scripts/maternal_perinatal_analyses/cohort_analysis/cohort_interventions_group_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 = 562661
+        self.start_date = Date(2024, 1, 1)
+        self.end_date = Date(2025, 1, 2)
+        self.pop_size = 12_000
+        self.number_of_draws = 11
+        self.runs_per_draw = 20
+
+    def log_configuration(self):
+        return {
+            'filename': 'block_intervention_group_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):
+        if draw_number == 0:
+            return {'PregnancySupervisor': {
+                    'analysis_year': 2024}}
+        else:
+            intervention_groups = [['oral_antihypertensives', 'iv_antihypertensives', 'mgso4'],
+                                   ['oral_antihypertensives', 'iv_antihypertensives', 'mgso4'],
+
+                                   ['amtsl', 'pph_treatment_uterotonics', 'pph_treatment_mrrp'],
+                                   ['amtsl', 'pph_treatment_uterotonics', 'pph_treatment_mrrp'],
+
+                                   ['post_abortion_care_core', 'ectopic_pregnancy_treatment'],
+                                   ['post_abortion_care_core', 'ectopic_pregnancy_treatment'],
+
+                                   ['caesarean_section', 'blood_transfusion', 'pph_treatment_surgery'],
+                                   ['caesarean_section', 'blood_transfusion', 'pph_treatment_surgery'],
+
+                                   ['abx_for_prom', 'sepsis_treatment', 'birth_kit'],
+                                   ['abx_for_prom', 'sepsis_treatment', 'birth_kit']]
+
+            avail_for_draw = [0.0, 1.0,
+                              0.0, 1.0,
+                              0.0, 1.0,
+                              0.0, 1.0,
+                              0.0, 1.0]
+
+            return {'PregnancySupervisor': {
+                    'analysis_year': 2024,
+                    'interventions_analysis': True,
+                    'interventions_under_analysis':intervention_groups[draw_number-1],
+                    'intervention_analysis_availability': avail_for_draw[draw_number-1]}}
+
+
+if __name__ == '__main__':
+    from tlo.cli import scenario_run
+    scenario_run([__file__])

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 = 562661
+        self.start_date = Date(2024, 1, 1)
+        self.end_date = Date(2025, 1, 2)
+        self.pop_size = 12_000
+        self.number_of_draws = 9
+        self.runs_per_draw = 30
+
+    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):
+        if draw_number == 0:
+            return {'PregnancySupervisor': {
+                    'analysis_year': 2024}}
+        else:
+            interventions_for_analysis = ['oral_antihypertensives', 'oral_antihypertensives',
+                                          'iv_antihypertensives', 'iv_antihypertensives',
+                                          'mgso4', 'mgso4',
+                                          'post_abortion_care_core', 'post_abortion_care_core']
+
+            avail_for_draw = [0.0, 1.0,
+                              0.0, 1.0,
+                              0.0, 1.0,
+                              0.0, 1.0]
+
+            return {'PregnancySupervisor': {
+                    'analysis_year': 2024,
+                    'interventions_analysis': True,
+                    'interventions_under_analysis':[interventions_for_analysis[draw_number-1]],
+                    'intervention_analysis_availability': avail_for_draw[draw_number-1]}}
+
+
+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
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from tlo.analysis.utils import extract_results, get_scenario_outputs, summarize
+
+outputspath = './outputs/sejjj49@ucl.ac.uk/'
+
+scenario = 'block_intervention_test-2024-11-06T145016Z'
+
+results_folder= get_scenario_outputs(scenario, 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 = extract_results(
+                results_folder,
+                module="tlo.methods.pregnancy_supervisor",
+                key=key,
+                custom_generate_series=sort_df,
+                do_scaling=False
+            )
+
+    results_df_summ = summarize(extract_results(
+        results_folder,
+        module="tlo.methods.pregnancy_supervisor",
+        key=key,
+        custom_generate_series=sort_df,
+        do_scaling=False
+    ))
+
+    return [results_df, results_df_summ]
+
+results = {k:get_data_frames(k, results_folder)[0] for k in
+           ['mat_comp_incidence', 'nb_comp_incidence', 'deaths_and_stillbirths','service_coverage',
+            'yearly_mnh_counter_dict']}
+
+results_sum = {k:get_data_frames(k, results_folder)[1] for k in
+               ['mat_comp_incidence', 'nb_comp_incidence', 'deaths_and_stillbirths','service_coverage',
+                'yearly_mnh_counter_dict']}
+
+def get_data(df, draw):
+    return (df.loc['direct_mmr', (draw, 'lower')],
+            df.loc['direct_mmr', (draw, 'mean')],
+            df.loc['direct_mmr', (draw, 'upper')])
+
+
+mmrs = {'baseline':get_data(results_sum['deaths_and_stillbirths'], 0),
+           # 'oral_antihypertensives_min':get_data(results_sum['deaths_and_stillbirths'], 1),
+           # 'oral_antihypertensives_max': get_data(results_sum['deaths_and_stillbirths'], 2),
+           # 'iv_antihypertensives_min':get_data(results_sum['deaths_and_stillbirths'], 3),
+           # 'iv_antihypertensives_max': get_data(results_sum['deaths_and_stillbirths'], 4),
+           # 'amtsl_min':get_data(results_sum['deaths_and_stillbirths'], 5),
+           # 'amtsl_max': get_data(results_sum['deaths_and_stillbirths'], 6),
+           'mgso4_min':get_data(results_sum['deaths_and_stillbirths'], 7),
+           'mgso4_max': get_data(results_sum['deaths_and_stillbirths'], 8),
+           # 'post_abortion_care_core_min':get_data(results_sum['deaths_and_stillbirths'], 9),
+           # 'post_abortion_care_core_max': get_data(results_sum['deaths_and_stillbirths'], 10),
+           # 'caesarean_section_min':get_data(results_sum['deaths_and_stillbirths'], 11),
+           # 'caesarean_section_max': get_data(results_sum['deaths_and_stillbirths'], 12),
+           # 'ectopic_pregnancy_treatment_min':get_data(results_sum['deaths_and_stillbirths'], 13),
+           # 'ectopic_pregnancy_treatment_max': get_data(results_sum['deaths_and_stillbirths'], 14),
+           }
+
+def get_mmr_diffs(df, draws):
+    diff_results = {}
+    baseline = results['deaths_and_stillbirths'][0]
+
+    for draw in draws:
+        # diff_df = ((results['deaths_and_stillbirths'][draw] - baseline)/baseline) * 100
+        diff_df = results['deaths_and_stillbirths'][draw] - baseline
+        diff_df.columns = pd.MultiIndex.from_tuples([(draw, v) for v in range(len(diff_df.columns))],
+                                                    names=['draw', 'run'])
+        results_diff = summarize(diff_df)
+        results_diff.fillna(0)
+        diff_results.update({draw: results_diff})
+
+    return diff_results
+
+diff_results = get_mmr_diffs(results, [7,8])
+
+
+results_diff = {#'oral_antihypertensives_min':get_data(diff_results[1], 1),
+#                 'oral_antihypertensives_max':get_data(diff_results[2], 2),
+#                 'iv_antihypertensives_min':get_data(diff_results[3], 3),
+#                 'iv_antihypertensives_max': get_data(diff_results[4], 4),
+#                 'amtsl_min':get_data(diff_results[5], 5),
+#                 'amtsl_max': get_data(diff_results[6], 6),
+                'mgso4_min':get_data(diff_results[7], 7),
+                'mgso4_max':get_data(diff_results[8], 8),
+                # 'post_abortion_care_core_min':get_data(diff_results[9], 9),
+                # 'post_abortion_care_core_max': get_data(diff_results[10], 10),
+                # 'caesarean_section_min':get_data(diff_results[11], 11),
+                # 'caesarean_section_max': get_data(diff_results[12], 12),
+                # 'ectopic_pregnancy_treatment_min':get_data(diff_results[13], 13),
+                # 'ectopic_pregnancy_treatment_max': get_data(diff_results[14], 14)
+                }
+
+# todo: compare deaths with demography logging...
+
+data = mmrs
+
+# Extract means and errors
+labels = data.keys()
+means = [vals[1] for vals in data.values()]
+lower_errors = [vals[1] - vals[0] for vals in data.values()]
+upper_errors = [vals[2] - vals[1] for vals in data.values()]
+errors = [lower_errors, upper_errors]
+
+# Create bar chart with error bars
+fig, ax = plt.subplots()
+ax.bar(labels, means, yerr=errors, capsize=5, alpha=0.7, ecolor='black')
+ax.set_ylabel('MMR')
+ax.set_title('Average MMR under each scenario')
+
+# Adjust label size
+plt.xticks(fontsize=8, rotation=90)
+plt.tight_layout()
+plt.show()
+
+#
+# # Example data with uncertainties
+# parameters = ['Blood Transfusion', 'Uterotonics', 'Sepsis treatment']
+# base_value = results_sum['deaths_and_stillbirths'].at['direct_mmr', (0, 'mean')]  # base case value for the output variable
+# high_values = [results_sum['deaths_and_stillbirths'].at['direct_mmr', (1, 'mean')],
+#               results_sum['deaths_and_stillbirths'].at['direct_mmr', (3, 'mean')],
+#               results_sum['deaths_and_stillbirths'].at['direct_mmr', (5, 'mean')]]  # lower-bound values for each parameter
+# low_values = [results_sum['deaths_and_stillbirths'].at['direct_mmr', (2, 'mean')],
+#               results_sum['deaths_and_stillbirths'].at['direct_mmr', (4, 'mean')],
+#               results_sum['deaths_and_stillbirths'].at['direct_mmr', (6, 'mean')]]  # upper-bound values for each parameter
+#
+# # Calculate deltas from base value
+# low_deltas = [base_value - lv for lv in low_values]
+# high_deltas = [hv - base_value for hv in high_values]
+#
+# # Sort parameters by absolute impact
+# abs_impacts = [abs(low) + abs(high) for low, high in zip(low_deltas, high_deltas)]
+# sorted_indices = np.argsort(abs_impacts)[::-1]
+# parameters = [parameters[i] for i in sorted_indices]
+# low_deltas = [low_deltas[i] for i in sorted_indices]
+# high_deltas = [high_deltas[i] for i in sorted_indices]
+#
+# # Calculate changes from the base case
+# low_deltas = [base_value - lv for lv in low_values]
+# high_deltas = [hv - base_value for hv in high_values]
+#
+# # Sort parameters by absolute impact (for a tornado effect)
+# abs_impacts = [abs(low) + abs(high) for low, high in zip(low_deltas, high_deltas)]
+# sorted_indices = np.argsort(abs_impacts)[::-1]
+# parameters = [parameters[i] for i in sorted_indices]
+# low_deltas = [low_deltas[i] for i in sorted_indices]
+# high_deltas = [high_deltas[i] for i in sorted_indices]
+#
+# # Plotting
+# fig, ax = plt.subplots(figsize=(8, 6))
+#
+# # Plot each bar for the low and high values
+# for i, (param, low, high) in enumerate(zip(parameters, low_deltas, high_deltas)):
+#     ax.barh(param, high, left=base_value, color='skyblue')
+#     ax.barh(param, low, left=base_value + low, color='salmon')
+#
+# # Reference line for base value
+# ax.axvline(base_value, color='black', linestyle='--', label="Base Value")
+#
+# # Labels and title
+# ax.set_xlabel('Output Variable')
+# ax.set_title('Tornado Plot')
+# plt.legend(['Base Value'])
+# plt.show()
+
+# 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()