Wasting Module

src/scripts/wasting_analyses/analysis_wasting.py

@@ -0,0 +1,199 @@
+"""
+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,
+)
+
+
+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()
+
+    def plot_wasting_prevalence(self):
+        w_prev_df = self.__logs_dict['wasting_prevalence_count']
+        w_prev_df.set_index(w_prev_df.date.dt.year, inplace=True)
+        w_prev_df.drop(columns='date', inplace=True)
+        w_prev_df = w_prev_df.apply(lambda _row: _row / _row.sum(), axis=1)
+        w_prev_df.plot(kind='bar', stacked=True,
+                       title="Wasting prevalence in children 0-59 months",
+                       ylabel='proportions',
+                       xlabel='year'
+                       )
+
+    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()
+        fig.savefig(
+            outputs / ('modal_gbd_deaths_by_gender' + datestamp + ".pdf"),
+            format="pdf"
+        )
+
+
+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 = 30000
+
+    # 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()
+
+    # plot wasting deaths by gender as compared to GBD deaths
+    wasting_analyses.plot_modal_gbd_deaths_by_gender()

src/tlo/analysis/utils.py

@@ -733,7 +733,6 @@ def get_color_coarse_appt(coarse_appt_type: str) -> str:
     '*': 'black',
 
     'FirstAttendance*': 'darkgrey',
-    'Inpatient*': 'silver',
 
     'Contraception*': 'darkseagreen',
     'AntenatalCare*': 'green',
@@ -804,6 +803,7 @@ 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',

src/tlo/methods/hsi_generic_first_appts.py

@@ -183,6 +183,9 @@ def do_at_generic_first_appt_non_emergency(hsi_event, squeeze_factor):
         if 'Stunting' in sim.modules:
             sim.modules['Stunting'].do_routine_assessment_for_chronic_undernutrition(person_id=person_id)
 
+        if 'Wasting' in sim.modules:
+            sim.modules['Wasting'].do_when_acute_malnutrition_assessment(person_id)
+
     else:
         # ----------------------------------- ADULT -----------------------------------
         if 'OesophagealCancer' in sim.modules: