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)