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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 35 new columns ({'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2023_januarydecember__15_years_to_30_years', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2023_januarydecember__46_years_to_60_years', 'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2024_januaryoctober__46_years_to_60_years', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2023_januarydecember__31_years_to_45_years', '2024__tb_patients_notified', '2022', 'tb_cases_notification_in_2024_january_to_october', '2023', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2023_januarydecember__0_to_14_years', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2023_januarydecember__15_years_to_30_years', 'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2024_januaryoctober__15_years_to_30_years', 'stateut', 'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2024_januaryoctober___60_years', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2024_januaryoctober__15_years_to_30_years', 'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2023_januarydecember__31_years_to_45_years', '2023__treated_successfully', 'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2023_januarydecember___60_years', 'source_file_rs_RS_Session_266_AU_1736_A_to_C_3 (1)_clean.csv', 'source_file_rs_RS_Session_266_AU_1736_A_to_C_4_clean.csv', '2023__tb_patients_notified', '2020', 'source_file', 'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2024_januaryoctober__0_to_14_years', 'source_file_rs_RS_Session_267_AU_3467_1_clean.csv', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2024_januaryoctober__46_years_to_60_years', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2024_januaryoctober__0_to_14_years', 'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2023_januarydecember__0_to_14_years', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2023_januarydecember__60_years', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2024_januaryoctober__60_years', 'source_file_rs_RS_Session_266_AU_2511_1_clean.csv', 'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2023_januarydecember__46_years_to_60_years', '2021', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2024_januaryoctober__31_years_to_45_years', 'tb_deaths__2024_january_to_october', 'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2024_januaryoctober__31_years_to_45_years'}) and 8 missing columns ({'risk_score', 'risk_z', 'system_score', 'mcmc_missed_ci_high', 'system_z', 'mcmc_missed_mean', 'mcmc_missed_ci_low', 'state'}).
This happened while the csv dataset builder was generating data using
hf://datasets/hssling/india-tb-missed-cases-analysis/merged_rs_tb_state.csv (at revision 4c7281cb886e64ee8939d6d3330a7f5d3f393e94), [/tmp/hf-datasets-cache/medium/datasets/31420983499196-config-parquet-and-info-hssling-india-tb-missed-c-6120300a/hub/datasets--hssling--india-tb-missed-cases-analysis/snapshots/4c7281cb886e64ee8939d6d3330a7f5d3f393e94/integrated_mcmc_system_risk.csv (origin=hf://datasets/hssling/india-tb-missed-cases-analysis@4c7281cb886e64ee8939d6d3330a7f5d3f393e94/integrated_mcmc_system_risk.csv), /tmp/hf-datasets-cache/medium/datasets/31420983499196-config-parquet-and-info-hssling-india-tb-missed-c-6120300a/hub/datasets--hssling--india-tb-missed-cases-analysis/snapshots/4c7281cb886e64ee8939d6d3330a7f5d3f393e94/merged_rs_tb_state.csv (origin=hf://datasets/hssling/india-tb-missed-cases-analysis@4c7281cb886e64ee8939d6d3330a7f5d3f393e94/merged_rs_tb_state.csv), /tmp/hf-datasets-cache/medium/datasets/31420983499196-config-parquet-and-info-hssling-india-tb-missed-c-6120300a/hub/datasets--hssling--india-tb-missed-cases-analysis/snapshots/4c7281cb886e64ee8939d6d3330a7f5d3f393e94/nfhs5_state_agg.csv (origin=hf://datasets/hssling/india-tb-missed-cases-analysis@4c7281cb886e64ee8939d6d3330a7f5d3f393e94/nfhs5_state_agg.csv), /tmp/hf-datasets-cache/medium/datasets/31420983499196-config-parquet-and-info-hssling-india-tb-missed-c-6120300a/hub/datasets--hssling--india-tb-missed-cases-analysis/snapshots/4c7281cb886e64ee8939d6d3330a7f5d3f393e94/nfhs_rs_tb_merged.csv (origin=hf://datasets/hssling/india-tb-missed-cases-analysis@4c7281cb886e64ee8939d6d3330a7f5d3f393e94/nfhs_rs_tb_merged.csv), /tmp/hf-datasets-cache/medium/datasets/31420983499196-config-parquet-and-info-hssling-india-tb-missed-c-6120300a/hub/datasets--hssling--india-tb-missed-cases-analysis/snapshots/4c7281cb886e64ee8939d6d3330a7f5d3f393e94/pca_integrated_analysis.csv (origin=hf://datasets/hssling/india-tb-missed-cases-analysis@4c7281cb886e64ee8939d6d3330a7f5d3f393e94/pca_integrated_analysis.csv), /tmp/hf-datasets-cache/medium/datasets/31420983499196-config-parquet-and-info-hssling-india-tb-missed-c-6120300a/hub/datasets--hssling--india-tb-missed-cases-analysis/snapshots/4c7281cb886e64ee8939d6d3330a7f5d3f393e94/state_missed_cases_latest.csv (origin=hf://datasets/hssling/india-tb-missed-cases-analysis@4c7281cb886e64ee8939d6d3330a7f5d3f393e94/state_missed_cases_latest.csv), /tmp/hf-datasets-cache/medium/datasets/31420983499196-config-parquet-and-info-hssling-india-tb-missed-c-6120300a/hub/datasets--hssling--india-tb-missed-cases-analysis/snapshots/4c7281cb886e64ee8939d6d3330a7f5d3f393e94/who_india_ts.csv (origin=hf://datasets/hssling/india-tb-missed-cases-analysis@4c7281cb886e64ee8939d6d3330a7f5d3f393e94/who_india_ts.csv)]
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 674, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
stateut: string
2020: int64
2021: int64
2022: int64
2023: int64
source_file: string
percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2023_januarydecember__0_to_14_years: int64
percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2023_januarydecember__15_years_to_30_ye (... 3 chars omitted): int64
percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2023_januarydecember__31_years_to_45_ye (... 3 chars omitted): int64
percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2023_januarydecember__46_years_to_60_ye (... 3 chars omitted): int64
percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2023_januarydecember___60_years: int64
percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2024_januaryoctober__0_to_14_years: int64
percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2024_januaryoctober__15_years_to_30_yea (... 2 chars omitted): int64
percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2024_januaryoctober__31_years_to_45_yea (... 2 chars omitted): int64
percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2024_januaryoctober__46_years_to_60_yea (... 2 chars omitted): int64
percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2024_januaryoctober___60_years: int64
source_file_rs_RS_Session_266_AU_1736_A_to_C_3 (1)_clean.csv: string
percentage_of_tb_deaths_of_total_tb_cases_reported_in_2023_januarydecember__0_to_14_years: double
percentage_of_tb_deaths_of_total_tb_cases_reported_in_2023_januarydecember__15_years_to_30_years: double
percentage_of_tb_deaths_of_total_tb_cases_reported_in_2023_januarydecember__31_years_to_45_years: double
percentage_of_tb_deaths_of_total_tb_cases_reported_in_2023_januarydecember__46_years_to_60_years: double
percentage_of_tb_deaths_of_total_tb_cases_reported_in_2023_januarydecember__60_years: double
percentage_of_tb_deaths_of_total_tb_cases_reported_in_2024_januaryoctober__0_to_14_years: double
percentage_of_tb_deaths_of_total_tb_cases_reported_in_2024_januaryoctober__15_years_to_30_years: double
percentage_of_tb_deaths_of_total_tb_cases_reported_in_2024_januaryoctober__31_years_to_45_years: double
percentage_of_tb_deaths_of_total_tb_cases_reported_in_2024_januaryoctober__46_years_to_60_years: double
percentage_of_tb_deaths_of_total_tb_cases_reported_in_2024_januaryoctober__60_years: double
source_file_rs_RS_Session_266_AU_1736_A_to_C_4_clean.csv: string
tb_cases_notification_in_2024_january_to_october: int64
tb_deaths__2024_january_to_october: int64
source_file_rs_RS_Session_266_AU_2511_1_clean.csv: string
2023__tb_patients_notified: int64
2023__treated_successfully: int64
2024__tb_patients_notified: int64
source_file_rs_RS_Session_267_AU_3467_1_clean.csv: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 8341
to
{'state': Value('string'), 'mcmc_missed_mean': Value('float64'), 'mcmc_missed_ci_low': Value('float64'), 'mcmc_missed_ci_high': Value('float64'), 'system_score': Value('float64'), 'risk_score': Value('float64'), 'system_z': Value('float64'), 'risk_z': Value('float64')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1889, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 35 new columns ({'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2023_januarydecember__15_years_to_30_years', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2023_januarydecember__46_years_to_60_years', 'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2024_januaryoctober__46_years_to_60_years', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2023_januarydecember__31_years_to_45_years', '2024__tb_patients_notified', '2022', 'tb_cases_notification_in_2024_january_to_october', '2023', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2023_januarydecember__0_to_14_years', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2023_januarydecember__15_years_to_30_years', 'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2024_januaryoctober__15_years_to_30_years', 'stateut', 'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2024_januaryoctober___60_years', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2024_januaryoctober__15_years_to_30_years', 'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2023_januarydecember__31_years_to_45_years', '2023__treated_successfully', 'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2023_januarydecember___60_years', 'source_file_rs_RS_Session_266_AU_1736_A_to_C_3 (1)_clean.csv', 'source_file_rs_RS_Session_266_AU_1736_A_to_C_4_clean.csv', '2023__tb_patients_notified', '2020', 'source_file', 'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2024_januaryoctober__0_to_14_years', 'source_file_rs_RS_Session_267_AU_3467_1_clean.csv', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2024_januaryoctober__46_years_to_60_years', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2024_januaryoctober__0_to_14_years', 'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2023_januarydecember__0_to_14_years', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2023_januarydecember__60_years', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2024_januaryoctober__60_years', 'source_file_rs_RS_Session_266_AU_2511_1_clean.csv', 'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2023_januarydecember__46_years_to_60_years', '2021', 'percentage_of_tb_deaths_of_total_tb_cases_reported_in_2024_januaryoctober__31_years_to_45_years', 'tb_deaths__2024_january_to_october', 'percentage_of_tb_cases_out_of_the_total_tb_cases_notified_in_2024_januaryoctober__31_years_to_45_years'}) and 8 missing columns ({'risk_score', 'risk_z', 'system_score', 'mcmc_missed_ci_high', 'system_z', 'mcmc_missed_mean', 'mcmc_missed_ci_low', 'state'}).
This happened while the csv dataset builder was generating data using
hf://datasets/hssling/india-tb-missed-cases-analysis/merged_rs_tb_state.csv (at revision 4c7281cb886e64ee8939d6d3330a7f5d3f393e94), [/tmp/hf-datasets-cache/medium/datasets/31420983499196-config-parquet-and-info-hssling-india-tb-missed-c-6120300a/hub/datasets--hssling--india-tb-missed-cases-analysis/snapshots/4c7281cb886e64ee8939d6d3330a7f5d3f393e94/integrated_mcmc_system_risk.csv (origin=hf://datasets/hssling/india-tb-missed-cases-analysis@4c7281cb886e64ee8939d6d3330a7f5d3f393e94/integrated_mcmc_system_risk.csv), /tmp/hf-datasets-cache/medium/datasets/31420983499196-config-parquet-and-info-hssling-india-tb-missed-c-6120300a/hub/datasets--hssling--india-tb-missed-cases-analysis/snapshots/4c7281cb886e64ee8939d6d3330a7f5d3f393e94/merged_rs_tb_state.csv (origin=hf://datasets/hssling/india-tb-missed-cases-analysis@4c7281cb886e64ee8939d6d3330a7f5d3f393e94/merged_rs_tb_state.csv), /tmp/hf-datasets-cache/medium/datasets/31420983499196-config-parquet-and-info-hssling-india-tb-missed-c-6120300a/hub/datasets--hssling--india-tb-missed-cases-analysis/snapshots/4c7281cb886e64ee8939d6d3330a7f5d3f393e94/nfhs5_state_agg.csv (origin=hf://datasets/hssling/india-tb-missed-cases-analysis@4c7281cb886e64ee8939d6d3330a7f5d3f393e94/nfhs5_state_agg.csv), /tmp/hf-datasets-cache/medium/datasets/31420983499196-config-parquet-and-info-hssling-india-tb-missed-c-6120300a/hub/datasets--hssling--india-tb-missed-cases-analysis/snapshots/4c7281cb886e64ee8939d6d3330a7f5d3f393e94/nfhs_rs_tb_merged.csv (origin=hf://datasets/hssling/india-tb-missed-cases-analysis@4c7281cb886e64ee8939d6d3330a7f5d3f393e94/nfhs_rs_tb_merged.csv), /tmp/hf-datasets-cache/medium/datasets/31420983499196-config-parquet-and-info-hssling-india-tb-missed-c-6120300a/hub/datasets--hssling--india-tb-missed-cases-analysis/snapshots/4c7281cb886e64ee8939d6d3330a7f5d3f393e94/pca_integrated_analysis.csv (origin=hf://datasets/hssling/india-tb-missed-cases-analysis@4c7281cb886e64ee8939d6d3330a7f5d3f393e94/pca_integrated_analysis.csv), /tmp/hf-datasets-cache/medium/datasets/31420983499196-config-parquet-and-info-hssling-india-tb-missed-c-6120300a/hub/datasets--hssling--india-tb-missed-cases-analysis/snapshots/4c7281cb886e64ee8939d6d3330a7f5d3f393e94/state_missed_cases_latest.csv (origin=hf://datasets/hssling/india-tb-missed-cases-analysis@4c7281cb886e64ee8939d6d3330a7f5d3f393e94/state_missed_cases_latest.csv), /tmp/hf-datasets-cache/medium/datasets/31420983499196-config-parquet-and-info-hssling-india-tb-missed-c-6120300a/hub/datasets--hssling--india-tb-missed-cases-analysis/snapshots/4c7281cb886e64ee8939d6d3330a7f5d3f393e94/who_india_ts.csv (origin=hf://datasets/hssling/india-tb-missed-cases-analysis@4c7281cb886e64ee8939d6d3330a7f5d3f393e94/who_india_ts.csv)]
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
state string | mcmc_missed_mean float64 | mcmc_missed_ci_low float64 | mcmc_missed_ci_high float64 | system_score float64 | risk_score float64 | system_z float64 | risk_z float64 |
|---|---|---|---|---|---|---|---|
Andhra Pradesh | 12,925.020457 | 0 | 69,271.732887 | 0.78855 | -0.773924 | 0.439777 | -0.609179 |
Arunachal Pradesh | 46,954.254885 | 35,374.853939 | 92,120.647164 | 0.68335 | -0.181239 | -0.605814 | -0.142658 |
Assam | 70,023.047341 | 27,246.188867 | 97,514.590413 | 0.59565 | 1.39434 | -1.477471 | 1.097527 |
Bihar | 1,098,951.106457 | 159,024.551816 | 1,656,742.101919 | 0.5329 | 2.418403 | -2.101148 | 1.903598 |
Chandigarh | 63,727.821723 | 45,233.865065 | 91,611.331701 | 0.76895 | -2.211048 | 0.244971 | -1.740382 |
Chhattisgarh | 29,996.707105 | 0 | 61,556.376962 | 0.7278 | 1.554516 | -0.164022 | 1.223607 |
Goa | 50,528.821484 | 33,495.962934 | 64,122.825502 | 0.73825 | -1.641616 | -0.060159 | -1.292165 |
Gujarat | 5,802.213783 | 0 | 59,110.728568 | 0.8273 | 0.929688 | 0.824916 | 0.731785 |
Haryana | 3,876.035586 | 0 | 14,028.852401 | 0.78585 | -0.895085 | 0.412941 | -0.704548 |
Himachal Pradesh | 147,724.249295 | 104,766.960081 | 196,605.60692 | 0.92685 | -0.154141 | 1.814351 | -0.121329 |
Jharkhand | 47,518.073238 | 25,621.343376 | 69,340.199381 | 0.6674 | 2.527776 | -0.764343 | 1.989688 |
Karnataka | 28,035.500593 | 4,244.181415 | 56,745.343057 | 0.7666 | -0.244069 | 0.221614 | -0.192114 |
Kerala | 29,637.265577 | 13,484.26464 | 90,607.736035 | 0.78145 | -2.119734 | 0.369209 | -1.668506 |
Ladakh | 27,220.808246 | 20,199.658664 | 38,584.774472 | 0.79005 | 1.073387 | 0.454685 | 0.844895 |
Lakshadweep | 101,164.743659 | 72,106.251831 | 136,143.900945 | 0.8864 | -1.450346 | 1.412315 | -1.141611 |
Madhya Pradesh | 2,509.889019 | 0 | 18,295.937451 | 0.57405 | 1.259471 | -1.692155 | 0.991368 |
Manipur | 81,570.123657 | 53,420.166212 | 164,356.173574 | 0.5986 | -0.003256 | -1.448151 | -0.002563 |
Meghalaya | 26,184.306272 | 16,409.156711 | 53,208.297203 | 0.6357 | 0.894724 | -1.079411 | 0.704264 |
Mizoram | 53,665.103355 | 31,751.655337 | 119,575.354488 | 0.915 | -0.954788 | 1.696573 | -0.751542 |
Nagaland | 28,041.327621 | 18,235.756862 | 60,326.311387 | 0.74175 | 0.08343 | -0.025372 | 0.065671 |
Odisha | 16,542.432763 | 4,773.558622 | 52,430.912525 | 0.82455 | 1.745857 | 0.797583 | 1.374216 |
Puducherry | 77,861.984285 | 53,260.988531 | 101,272.39813 | 0.97575 | -1.944205 | 2.300372 | -1.530342 |
Punjab | 24,273.611321 | 4,208.979215 | 78,921.339361 | 0.6717 | -1.524222 | -0.721605 | -1.199761 |
Rajasthan | 220,526.594407 | 64,336.767691 | 338,059.46898 | 0.71425 | 0.833288 | -0.298697 | 0.655906 |
Sikkim | 56,450.79197 | 32,652.381539 | 68,247.122118 | 0.6929 | -1.123248 | -0.510896 | -0.884142 |
Tamil Nadu | 3,698.472243 | 0 | 23,829.491153 | 0.7904 | -0.828842 | 0.458164 | -0.652407 |
Telangana | 14,209.557012 | 0 | 46,031.38048 | 0.8373 | -0.15007 | 0.924307 | -0.118124 |
Tripura | 43,908.317232 | 32,036.625511 | 63,826.701795 | 0.60115 | 1.412066 | -1.422806 | 1.11148 |
Uttar Pradesh | 15,104.334087 | 0 | 75,204.264324 | 0.62415 | 0.887026 | -1.194208 | 0.698205 |
Uttarakhand | 8,078.04463 | 0 | 34,454.939812 | 0.74415 | -0.432351 | -0.001518 | -0.340316 |
West Bengal | 2,402.629046 | 0 | 12,197.970091 | 0.7606 | 1.237436 | 0.161979 | 0.974023 |
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India TB Missed Cases Analysis & Living Model (2025)
🌟 Project Overview
This repository hosts a comprehensive, multi-method analytical framework designed to estimate and understand the "missing" millions of Tuberculosis (TB) cases in India. By integrating Bayesian statistics, Dimensionality Reduction (PCA), and Causal Inference (DAG), this project provides a high-resolution view of TB detection determinants across Indian states.
Core Analytical Pillars:
- Bayesian MCMC Estimation: Hierarchical probabilistic modeling to quantify national and state-level missed cases with 95% Credible Intervals.
- Principal Component Analysis (PCA): Construction of data-driven 'System Strength' and 'Risk Burden' indices, explaining 2.8x more variance than traditional indicators.
- Causal Directed Acyclic Graphs (DAG): Structural mapping of 36 causal pathways across 26 variables to identify intervention priorities.
🚀 The Living Model (living_tb_analysis_model.ipynb)
The centerpiece of this project is a "Living" Bayesian Notebook. Unlike static reports, this model is designed for continuous refinement:
- Autonomous Updates: Fetches latest data from WHO Global Health Observatory APIs and Ni-kshay reports.
- Incremental Learning: Uses the project's 2023 estimates as priors, fine-tuning the posterior as 2024/2025 data becomes available.
- Policy Priority Matrix: Automatically regenerates state-specific intervention strategies based on the latest data.
📊 Dataset Contents
1. Primary Analysis Data
pca_integrated_analysis.csv: The final merged dataset with PCA components.integrated_mcmc_system_risk.csv: Pre-calculated indices for system-risk correlation analysis.state_missed_cases_latest.csv: Final state-level estimates for 2023.
2. Raw Harmonized Sources
nfhs5_state_agg.csv: Aggregated nutrition and risk behavior indicators from India's NFHS-5 survey.who_india_ts.csv: Historical TB incidence and notification trends for India (2000-2023).
3. Model Results (JSON)
mcmc_bayesian_results.json: Summary of posterior distributions.mcmc_missed_cases_sensitivity_results.json: Sensitivity analysis findings.
🛠️ Usage Instructions
Academic Researchers
Load the CSV files into Python/R to study the relationship between healthcare infrastructure (System_PC1) and nutritional vulnerabilities (Risk_PC1).
import pandas as pd
df = pd.read_csv('pca_integrated_analysis.csv')
# Analyze correlation between System PC1 and Missed Cases
print(df[['System_PC1', 'missed_cases_mean']].corr())
Policy Makers
Refer to the living_tb_analysis_model.ipynb output (specifically the State Prioritization Section) to identify whether a state requires "System Strengthening" or "Risk Management" focus.
📖 Citation
@misc{siddalingaiah2025tb,
author = {Siddalingaiah, H. S.},
title = {Advanced Multi-Method Analysis of Missed Tuberculosis Cases in India},
year = {2025},
note = {MCMC-PCA-DAG Framework}
}
⚖️ License
This work is released under CC0: Public Domain. Use, modify, and distribute freely to support the global mission to End TB.
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