def main(): make_output_directories(FIGURE_PATH) uncertainty_df = load_uncertainty_table(DATA_PATH) # End TB Targets print("End TB Targets") for output in ["incidence", "mortality"]: print_median_and_ci(uncertainty_df, output, 2015, 0) # main outputs, national level for output in ["incidence", "mortality"]: for scenario in [1, 0]: print_median_and_ci(uncertainty_df, output, 2020, scenario) # regional level for region in ["majuro", "ebeye"]: for year in [2020, 2050]: for scenario in [1, 0]: print_median_and_ci(uncertainty_df, f"incidenceXlocation_{region}", year, scenario) # diabetes scenarios print() print("Diabetes scenarios") for scenario in [9, 10]: print_median_and_ci(uncertainty_df, "incidence", 2050, scenario) # PT in all contacts print() print("PT in all contacts") for output in ["incidence", "mortality"]: print_median_and_ci(uncertainty_df, output, 2050, 11)
def main(): make_output_directories(FIGURE_PATH) file_path = os.path.join(DATA_PATH, "posterior_centiles.csv") posterior_df = pd.read_csv(file_path, sep=",") posterior_df = posterior_df.rename( columns={ "Unnamed: 0": "Parameter", "2.5": "2.5th percentile", "50.0": "Median", "97.5": "97.5th percentile", }) make_posterior_table(posterior_df)
def main(): make_output_directories(FIGURE_PATH) get_format() uncertainty_df = load_uncertainty_table(DATA_PATH) for is_logscale in [True, False]: plot_elimination(uncertainty_df, is_logscale)
def main(): make_output_directories(FIGURE_PATH) get_format() uncertainty_df = load_uncertainty_table(DATA_PATH) plot_screening_rate(uncertainty_df) plot_model_fits(uncertainty_df)
def main(): make_output_directories(FIGURE_PATH) get_format() uncertainty_df = load_uncertainty_table(DATA_PATH) plot_counterfactual(uncertainty_df)
def main(): make_output_directories(FIGURE_PATH) make_priors_table(PRIORS)
def main(): make_output_directories(FIGURE_PATH) get_format() uncertainty_df = load_uncertainty_table(DATA_PATH) plot_diabetes_graph(uncertainty_df)