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Let the Scale-up of HIV, TB, Malaria services go to "MAXIMAL" levels …
…(as well as target levels) (#1443) * create new scenario file for testing HTM max scale-up update ResourceFile_HIV.xlsx, ResourceFile_malaria.xlsx, ResourceFile_TB.xlsx to include max scale-up * change parameter do_sacleup to categorical in hiv, tb and malaria modules * add type_of_scaleup with default value=none to htm resourcefiles * choose scale-up values from resourcefile using parameter type_of_scaleup * set up scenarios to test max scale-up works as expected * set up scenarios to test max scale-up works as expected * edit figures * switch type_of_scaleup to string * update test_htm_scaleup.py * enhanced_lifestly.py update np.timedelta64 as ValueError: Unit M is not supported. Only unambiguous timedelta values durations are supported. Allowed units are 'W', 'D', 'h', 'm', 's', 'ms', 'us', 'ns' * switch scale-up parameter selection (target vs max) to update_parameters_for_program_scaleup not within read_parameters as this is read before the analysis script updates parameters * comment out plots in analysis_maxHTM_scenario.py * tidy up script * move import statements used for plotting * remove unneeded columns from scale-up_parameters sheet in resourcefiles * switch to using maximum scale-up * roll back change in enhanced_lifestyle.py --------- Co-authored-by: tdm32 <[email protected]>
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src/scripts/comparison_of_horizontal_and_vertical_programs/analysis_maxHTM_scenario.py
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| """ | ||
| This scenario file sets up the scenarios for simulating the effects of scaling up programs | ||
| The scenarios are: | ||
| *0 baseline mode 1 | ||
| *1 scale-up HIV program | ||
| *2 scale-up TB program | ||
| *3 scale-up malaria program | ||
| *4 scale-up HIV and Tb and malaria programs | ||
| scale-up occurs on the default scale-up start date (01/01/2025: in parameters list of resourcefiles) | ||
| For all scenarios, keep all default health system settings | ||
| check the batch configuration gets generated without error: | ||
| tlo scenario-run --draw-only src/scripts/comparison_of_horizontal_and_vertical_programs/analysis_maxHTM_scenario.py | ||
| Run on the batch system using: | ||
| tlo batch-submit src/scripts/comparison_of_horizontal_and_vertical_programs/analysis_maxHTM_scenario.py | ||
| or locally using: | ||
| tlo scenario-run src/scripts/comparison_of_horizontal_and_vertical_programs/analysis_maxHTM_scenario.py | ||
| or execute a single run: | ||
| tlo scenario-run src/scripts/comparison_of_horizontal_and_vertical_programs/analysis_maxHTM_scenario.py --draw 1 0 | ||
| """ | ||
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| import datetime | ||
| from pathlib import Path | ||
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| from tlo import Date, logging | ||
| from tlo.methods import ( | ||
| demography, | ||
| enhanced_lifestyle, | ||
| epi, | ||
| healthburden, | ||
| healthseekingbehaviour, | ||
| healthsystem, | ||
| hiv, | ||
| malaria, | ||
| simplified_births, | ||
| symptommanager, | ||
| tb, | ||
| ) | ||
| from tlo.scenario import BaseScenario | ||
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| resourcefilepath = Path("./resources") | ||
| datestamp = datetime.date.today().strftime("__%Y_%m_%d") | ||
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| outputspath = Path("./outputs") | ||
| scaleup_start_year = 2012 | ||
| end_date = Date(2015, 1, 1) | ||
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| class EffectOfProgrammes(BaseScenario): | ||
| def __init__(self): | ||
| super().__init__() | ||
| self.seed = 0 | ||
| self.start_date = Date(2010, 1, 1) | ||
| self.end_date = end_date | ||
| self.pop_size = 1_000 | ||
| self.number_of_draws = 5 | ||
| self.runs_per_draw = 1 | ||
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| def log_configuration(self): | ||
| return { | ||
| 'filename': 'scaleup_tests', | ||
| 'directory': Path('./outputs'), # <- (specified only for local running) | ||
| 'custom_levels': { | ||
| '*': logging.WARNING, | ||
| 'tlo.methods.hiv': logging.INFO, | ||
| 'tlo.methods.tb': logging.INFO, | ||
| 'tlo.methods.malaria': logging.INFO, | ||
| 'tlo.methods.demography': logging.INFO, | ||
| } | ||
| } | ||
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| def modules(self): | ||
| return [ | ||
| demography.Demography(resourcefilepath=self.resources), | ||
| simplified_births.SimplifiedBirths(resourcefilepath=self.resources), | ||
| enhanced_lifestyle.Lifestyle(resourcefilepath=self.resources), | ||
| healthsystem.HealthSystem(resourcefilepath=self.resources), | ||
| symptommanager.SymptomManager(resourcefilepath=self.resources), | ||
| healthseekingbehaviour.HealthSeekingBehaviour(resourcefilepath=self.resources), | ||
| healthburden.HealthBurden(resourcefilepath=self.resources), | ||
| epi.Epi(resourcefilepath=self.resources), | ||
| hiv.Hiv(resourcefilepath=self.resources), | ||
| tb.Tb(resourcefilepath=self.resources), | ||
| malaria.Malaria(resourcefilepath=self.resources), | ||
| ] | ||
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| def draw_parameters(self, draw_number, rng): | ||
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| return { | ||
| 'Hiv': { | ||
| 'type_of_scaleup': ['none', 'max', 'none', 'none', 'max'][draw_number], | ||
| 'scaleup_start_year': scaleup_start_year, | ||
| }, | ||
| 'Tb': { | ||
| 'type_of_scaleup': ['none', 'none', 'max', 'none', 'max'][draw_number], | ||
| 'scaleup_start_year': scaleup_start_year, | ||
| }, | ||
| 'Malaria': { | ||
| 'type_of_scaleup': ['none', 'none', 'none', 'max', 'max'][draw_number], | ||
| 'scaleup_start_year': scaleup_start_year, | ||
| }, | ||
| } | ||
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| if __name__ == '__main__': | ||
| from tlo.cli import scenario_run | ||
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| scenario_run([__file__]) | ||
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| # %% Produce some figures and summary info | ||
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| # import pandas as pd | ||
| # import matplotlib.pyplot as plt | ||
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| # # Find results_folder associated with a given batch_file (and get most recent [-1]) | ||
| # results_folder = get_scenario_outputs("scaleup_tests-", outputspath)[-1] | ||
| # | ||
| # # get basic information about the results | ||
| # info = get_scenario_info(results_folder) | ||
| # | ||
| # # 1) Extract the parameters that have varied over the set of simulations | ||
| # params = extract_params(results_folder) | ||
| # | ||
| # | ||
| # # DEATHS | ||
| # | ||
| # | ||
| # def get_num_deaths_by_cause_label(_df): | ||
| # """Return total number of Deaths by label within the TARGET_PERIOD | ||
| # values are summed for all ages | ||
| # df returned: rows=COD, columns=draw | ||
| # """ | ||
| # return _df \ | ||
| # .loc[pd.to_datetime(_df.date).between(*TARGET_PERIOD)] \ | ||
| # .groupby(_df['label']) \ | ||
| # .size() | ||
| # | ||
| # | ||
| # TARGET_PERIOD = (Date(scaleup_start_year, 1, 1), end_date) | ||
| # | ||
| # # produce df of total deaths over scale-up period | ||
| # num_deaths_by_cause_label = extract_results( | ||
| # results_folder, | ||
| # module='tlo.methods.demography', | ||
| # key='death', | ||
| # custom_generate_series=get_num_deaths_by_cause_label, | ||
| # do_scaling=True | ||
| # ) | ||
| # | ||
| # | ||
| # def summarise_deaths_for_one_cause(results_folder, label): | ||
| # """ returns mean deaths for each year of the simulation | ||
| # values are aggregated across the runs of each draw | ||
| # for the specified cause | ||
| # """ | ||
| # | ||
| # results_deaths = extract_results( | ||
| # results_folder, | ||
| # module="tlo.methods.demography", | ||
| # key="death", | ||
| # custom_generate_series=( | ||
| # lambda df: df.assign(year=df["date"].dt.year).groupby( | ||
| # ["year", "label"])["person_id"].count() | ||
| # ), | ||
| # do_scaling=True, | ||
| # ) | ||
| # # removes multi-index | ||
| # results_deaths = results_deaths.reset_index() | ||
| # | ||
| # # select only cause specified | ||
| # tmp = results_deaths.loc[ | ||
| # (results_deaths.label == label) | ||
| # ] | ||
| # | ||
| # # group deaths by year | ||
| # tmp = pd.DataFrame(tmp.groupby(["year"]).sum()) | ||
| # | ||
| # # get mean for each draw | ||
| # mean_deaths = pd.concat({'mean': tmp.iloc[:, 1:].groupby(level=0, axis=1).mean()}, axis=1).swaplevel(axis=1) | ||
| # | ||
| # return mean_deaths | ||
| # | ||
| # | ||
| # aids_deaths = summarise_deaths_for_one_cause(results_folder, 'AIDS') | ||
| # tb_deaths = summarise_deaths_for_one_cause(results_folder, 'TB (non-AIDS)') | ||
| # malaria_deaths = summarise_deaths_for_one_cause(results_folder, 'Malaria') | ||
| # | ||
| # | ||
| # draw_labels = ['No scale-up', 'HIV scale-up', 'TB scale-up', 'Malaria scale-up', 'HTM scale-up'] | ||
| # colours = ['blue', 'green', 'red', 'purple', 'orange'] | ||
| # | ||
| # # Create subplots | ||
| # fig, axs = plt.subplots(3, 1, figsize=(10, 10)) | ||
| # # Plot for df1 | ||
| # for i, col in enumerate(aids_deaths.columns): | ||
| # axs[0].plot(aids_deaths.index, aids_deaths[col], label=draw_labels[i], | ||
| # color=colours[i]) | ||
| # axs[0].set_title('HIV/AIDS') | ||
| # axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5)) # Legend to the right of the plot | ||
| # axs[0].axvline(x=scaleup_start_year, color='gray', linestyle='--') | ||
| # | ||
| # # Plot for df2 | ||
| # for i, col in enumerate(tb_deaths.columns): | ||
| # axs[1].plot(tb_deaths.index, tb_deaths[col], color=colours[i]) | ||
| # axs[1].set_title('TB') | ||
| # axs[1].axvline(x=scaleup_start_year, color='gray', linestyle='--') | ||
| # | ||
| # # Plot for df3 | ||
| # for i, col in enumerate(malaria_deaths.columns): | ||
| # axs[2].plot(malaria_deaths.index, malaria_deaths[col], color=colours[i]) | ||
| # axs[2].set_title('Malaria') | ||
| # axs[2].axvline(x=scaleup_start_year, color='gray', linestyle='--') | ||
| # | ||
| # for ax in axs: | ||
| # ax.set_xlabel('Years') | ||
| # ax.set_ylabel('Number deaths') | ||
| # | ||
| # plt.tight_layout(rect=[0, 0, 0.85, 1]) # Adjust layout to make space for legend | ||
| # plt.show() | ||
| # |
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