Wasting Module

src/scripts/wasting_analyses/analysis_wasting.py

@@ -0,0 +1,252 @@
+"""
+An analysis file for the wasting module
+"""
+import datetime
+# %% Import statements
+from pathlib import Path
+
+import pandas as pd
+from matplotlib import pyplot as plt
+
+from tlo import Date, Simulation, logging
+from tlo.analysis.utils import compare_number_of_deaths, parse_log_file
+from tlo.methods import (
+    care_of_women_during_pregnancy,
+    contraception,
+    demography,
+    enhanced_lifestyle,
+    epi,
+    healthburden,
+    healthseekingbehaviour,
+    healthsystem,
+    hiv,
+    labour,
+    newborn_outcomes,
+    postnatal_supervisor,
+    pregnancy_supervisor,
+    symptommanager,
+    tb,
+    wasting,
+)
+
+
+def add_footnote(fig: plt.Figure, footnote: str):
+    """ A function that adds a footnote below each plot. Here we are explaining what a denominator for every
+    graph is """
+    fig.figure.text(0.5, 0.01, footnote, ha="center", fontsize=10,
+                    bbox={"facecolor": "gray", "alpha": 0.3, "pad": 5})
+
+
+class WastingAnalyses:
+    """
+    This class looks at plotting all important outputs from the wasting module
+    """
+
+    def __init__(self, log_file_path):
+        self.__log_file_path = log_file_path
+        # parse wasting logs
+        self.__logs_dict = \
+            parse_log_file(self.__log_file_path)['tlo.methods.wasting']
+
+        # gender description
+        self.__gender_desc = {'M': 'Males',
+                              'F': 'Females'}
+
+        # wasting types description
+        self.__wasting_types_desc = {'WHZ<-3': 'severe wasting',
+                                     '-3<=WHZ<-2': 'moderate wasting',
+                                     'WHZ>=-2': 'not undernourished'}
+
+    def plot_wasting_incidence(self):
+        """ plot the incidence of wasting over time """
+        w_inc_df = self.__logs_dict['wasting_incidence_count']
+        w_inc_df.set_index(w_inc_df.date.dt.year, inplace=True)
+        w_inc_df.drop(columns='date', inplace=True)
+        # get age year. doesn't matter what wasting category you choose for
+        # they all have same age groups
+        age_years = list(w_inc_df.loc[w_inc_df.index[0], 'WHZ<-3'].keys(
+
+        ))
+        age_years.remove('5+y')
+
+        _row_counter = 0
+        _col_counter = 0
+        # plot setup
+        fig, axes = plt.subplots(nrows=2, ncols=3, sharex=True, sharey=True)
+        for _age in age_years:
+            new_df = pd.DataFrame()
+            for state in w_inc_df.columns:
+                new_df[state] = \
+                    w_inc_df.apply(lambda row: row[state][_age], axis=1)
+
+            new_df = new_df.apply(lambda _row: _row / _row.sum(), axis=1)
+            plotting = new_df[["WHZ<-3", "-3<=WHZ<-2"]]
+            # convert into proportions
+            ax = plotting.plot(kind='bar', stacked=True,
+                               ax=axes[_row_counter, _col_counter],
+                               title=f"incidence of wasting in {_age} infants",
+                               ylim=[0, 0.05])
+            ax.legend(self.__wasting_types_desc.values(), loc='lower right')
+            ax.set_xlabel('year')
+            ax.set_ylabel('proportions')
+            # move to another row
+            if _col_counter == 2:
+                _row_counter += 1
+                _col_counter = -1
+            _col_counter += 1  # increment column counter
+            fig.tight_layout()
+        fig.savefig(
+            outputs / ('wasting incidence' + datestamp + ".pdf"),
+            format="pdf"
+        )
+        plt.show()
+
+    def plot_wasting_prevalence_per_year(self):
+        """ plot wasting prevalence of all age groups per year. Proportions are obtained by getting a total number of
+        children wasted divide by the total number of children less than 5 years"""
+        w_prev_df = self.__logs_dict["wasting_prevalence_count"]
+        w_prev_df = w_prev_df[['date', 'total_under5_prop']]
+        w_prev_df.set_index(w_prev_df.date.dt.year, inplace=True)
+        w_prev_df.drop(columns='date', inplace=True)
+        fig, ax = plt.subplots()
+        w_prev_df["total_under5_prop"].plot(kind='bar', stacked=True,
+                                            ax=ax,
+                                            title="Wasting prevalence in children 0-59 months per year",
+                                            ylabel='proportions',
+                                            xlabel='year',
+                                            ylim=[0, 0.05])
+        add_footnote(fig, "Proportion = total number of wasted children < 5 years / total number of children < 5 years")
+        plt.tight_layout()
+        fig.savefig(
+            outputs / ('wasting_prevalence_per_year' + datestamp + ".pdf"),
+            format="pdf"
+        )
+        plt.show()
+
+    def plot_wasting_prevalence_by_age_group(self):
+        """ plot wasting prevalence per each age group. Proportions are obtained by getting a total number of
+        children wasted in a particular age-group divide by the total number of children per that age-group"""
+        w_prev_df = self.__logs_dict["wasting_prevalence_count"]
+        w_prev_df.drop(columns={'total_under5_prop'}, inplace=True)
+        w_prev_df.set_index(w_prev_df.date.dt.year, inplace=True)
+        w_prev_df = w_prev_df.loc[w_prev_df.index == 2023]
+        w_prev_df.drop(columns='date', inplace=True)
+        fig, ax = plt.subplots()
+        # plot wasting prevalence
+        w_prev_df.squeeze().plot(kind='bar', stacked=False,
+                                 ax=ax,
+                                 title="Wasting prevalence in children 0-59 months per each age group in 2023",
+                                 ylabel='proportions',
+                                 xlabel='year',
+                                 ylim=[0, 0.1])
+        add_footnote(fig, "Proportion = total number of wasted children < 5 years per each age-group / total number of "
+                          "children < 5 years per each age-group")
+        plt.tight_layout()
+        fig.savefig(
+            outputs / ('wasting_prevalence_per_each_age_group' + datestamp + ".pdf"),
+            format="pdf"
+        )
+        plt.show()
+
+    def plot_modal_gbd_deaths_by_gender(self):
+        """ compare modal and GBD deaths by gender """
+        death_compare = \
+            compare_number_of_deaths(self.__log_file_path, resources)
+        fig, axs = plt.subplots(nrows=1, ncols=2, sharey=True, sharex=True)
+        for _col, sex in enumerate(('M', 'F')):
+            plot_df = death_compare.loc[(['2010-2014', '2015-2019'],
+                                         sex, slice(None), 'Childhood Undernutrition'
+                                         )].groupby('period').sum()
+            plotting = plot_df.loc[['2010-2014', '2015-2019']]
+            ax = plotting['model'].plot.bar(label='Model', ax=axs[_col], rot=0)
+            ax.errorbar(x=plotting['model'].index, y=plotting.GBD_mean,
+                        yerr=[plotting.GBD_lower, plotting.GBD_upper],
+                        fmt='o', color='#000', label="GBD")
+            ax.set_title(f'{self.__gender_desc[sex]} '
+                         f'wasting deaths, 2010-2014')
+            ax.set_xlabel("Time period")
+            ax.set_ylabel("Number of deaths")
+            ax.legend(loc=2)
+        fig.tight_layout()
+        add_footnote(fig, "Model output against Global Burden of Diseases(GDB) study data")
+        fig.savefig(
+            outputs / ('modal_gbd_deaths_by_gender' + datestamp + ".pdf"),
+            format="pdf"
+        )
+        plt.show()
+
+
+if __name__ == "__main__":
+    seed = 1
+
+    # Path to the resource files used by the disease and intervention methods
+    resources = Path("./resources")
+    outputs = Path("./outputs")
+
+    # create a datestamp
+    datestamp = datetime.date.today().strftime("__%Y_%m_%d") + \
+                datetime.datetime.now().strftime("%H_%M_%S")
+
+    # configure logging
+    log_config = {
+        # output filename. A timestamp will be added to this.
+        "filename": "wasting",
+        "custom_levels": {  # Customise the output of specific loggers
+            "tlo.methods.demography": logging.INFO,
+            "tlo.methods.population": logging.INFO,
+            "tlo.methods.wasting": logging.INFO,
+            '*': logging.WARNING
+        }
+    }
+
+    # Basic arguments required for the simulation
+    start_date = Date(2010, 1, 1)
+    end_date = Date(2030, 1, 2)
+    pop_size = 20000
+
+    # Create simulation instance for this run.
+    sim = Simulation(start_date=start_date, seed=seed, log_config=log_config)
+
+    # Register modules for simulation
+    sim.register(
+        demography.Demography(resourcefilepath=resources),
+        healthsystem.HealthSystem(resourcefilepath=resources,
+                                  service_availability=['*'],
+                                  cons_availability='default'),
+        healthseekingbehaviour.HealthSeekingBehaviour(resourcefilepath=resources),
+        healthburden.HealthBurden(resourcefilepath=resources),
+        symptommanager.SymptomManager(resourcefilepath=resources),
+        enhanced_lifestyle.Lifestyle(resourcefilepath=resources),
+        labour.Labour(resourcefilepath=resources),
+        care_of_women_during_pregnancy.CareOfWomenDuringPregnancy(
+            resourcefilepath=resources),
+        contraception.Contraception(resourcefilepath=resources),
+        pregnancy_supervisor.PregnancySupervisor(resourcefilepath=resources),
+        postnatal_supervisor.PostnatalSupervisor(resourcefilepath=resources),
+        newborn_outcomes.NewbornOutcomes(resourcefilepath=resources),
+        hiv.Hiv(resourcefilepath=resources),
+        tb.Tb(resourcefilepath=resources),
+        epi.Epi(resourcefilepath=resources),
+        wasting.Wasting(resourcefilepath=resources),
+    )
+
+    sim.make_initial_population(n=pop_size)
+    sim.simulate(end_date=end_date)
+
+    # read the results
+    output_path = sim.log_filepath
+
+    # initialise the wasting class
+    wasting_analyses = WastingAnalyses(output_path)
+
+    # plot wasting incidence
+    wasting_analyses.plot_wasting_incidence()
+
+    # plot wasting prevalence
+    wasting_analyses.plot_wasting_prevalence_per_year()
+
+    # plot wasting prevalence by age group
+    wasting_analyses.plot_wasting_prevalence_by_age_group()
+
+    # plot wasting deaths by gender as compared to GBD deaths
+    wasting_analyses.plot_modal_gbd_deaths_by_gender()

src/scripts/wasting_analyses/scenario_wasting.py

@@ -0,0 +1,91 @@
+"""
+This file defines a scenario for wasting analysis.
+
+It can be submitted on Azure Batch by running:
+
+    tlo batch-submit src/scripts/wasting_analyses/scenario_wasting.py
+
+or locally using:
+
+    tlo scenario-run src/scripts/wasting_analyses/scenario_wasting.py
+"""
+import warnings
+
+from tlo import Date, logging
+from tlo.methods import (
+    care_of_women_during_pregnancy,
+    contraception,
+    demography,
+    enhanced_lifestyle,
+    epi,
+    healthburden,
+    healthseekingbehaviour,
+    healthsystem,
+    hiv,
+    labour,
+    newborn_outcomes,
+    postnatal_supervisor,
+    pregnancy_supervisor,
+    symptommanager,
+    tb,
+    wasting,
+)
+from tlo.scenario import BaseScenario
+
+# capture warnings during simulation run
+warnings.simplefilter('default', (UserWarning, RuntimeWarning))
+
+
+class WastingAnalysis(BaseScenario):
+
+    def __init__(self):
+        super().__init__(
+            seed=0,
+            start_date=Date(2010, 1, 1),
+            end_date=Date(2030, 1, 1),
+            initial_population_size=20_000,
+            number_of_draws=1,
+            runs_per_draw=1,
+        )
+
+    def log_configuration(self):
+        return {
+            'filename': 'wasting_analysis',
+            'directory': './outputs',
+            "custom_levels": {  # Customise the output of specific loggers
+                "tlo.methods.demography": logging.INFO,
+                "tlo.methods.population": logging.INFO,
+                "tlo.methods.wasting": logging.INFO,
+                '*': logging.WARNING
+            }
+        }
+
+    def modules(self):
+        return [demography.Demography(resourcefilepath=self.resources),
+                healthsystem.HealthSystem(resourcefilepath=self.resources,
+                                          service_availability=['*'],
+                                          cons_availability='default'),
+                healthseekingbehaviour.HealthSeekingBehaviour(resourcefilepath=self.resources),
+                healthburden.HealthBurden(resourcefilepath=self.resources),
+                symptommanager.SymptomManager(resourcefilepath=self.resources),
+                enhanced_lifestyle.Lifestyle(resourcefilepath=self.resources),
+                labour.Labour(resourcefilepath=self.resources),
+                care_of_women_during_pregnancy.CareOfWomenDuringPregnancy(
+                    resourcefilepath=self.resources),
+                contraception.Contraception(resourcefilepath=self.resources),
+                pregnancy_supervisor.PregnancySupervisor(resourcefilepath=self.resources),
+                postnatal_supervisor.PostnatalSupervisor(resourcefilepath=self.resources),
+                newborn_outcomes.NewbornOutcomes(resourcefilepath=self.resources),
+                hiv.Hiv(resourcefilepath=self.resources),
+                tb.Tb(resourcefilepath=self.resources),
+                epi.Epi(resourcefilepath=self.resources),
+                wasting.Wasting(resourcefilepath=self.resources)]
+
+    def draw_parameters(self, draw_number, rng):
+        return {}
+
+
+if __name__ == '__main__':
+    from tlo.cli import scenario_run
+
+    scenario_run([__file__])

src/tlo/analysis/utils.py

@@ -735,7 +735,6 @@ def get_color_coarse_appt(coarse_appt_type: str) -> str:
     '*': 'black',
 
     'FirstAttendance*': 'darkgrey',
-    'Inpatient*': 'silver',
 
     'Contraception*': 'darkseagreen',
     'AntenatalCare*': 'green',
@@ -806,6 +805,7 @@ def get_color_short_treatment_id(short_treatment_id: str) -> str:
 
     'Lower respiratory infections': 'darkorange',
     'Childhood Diarrhoea': 'tan',
+    'Childhood Undernutrition': 'tomato',
 
     'AIDS': 'deepskyblue',
     'Malaria': 'lightsteelblue',