indicator_code stringclasses 1
value | country_iso3 stringclasses 47
values | who_region stringclasses 1
value | year int64 1.97k 2.02k | value_numeric float64 0 5.26k | value_low null | value_high null | value_display stringlengths 1 4 | last_updated stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|
WHS3_46 | BWA | AFR | 2,009 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | RWA | AFR | 1,980 | 103 | null | null | 103 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | ZWE | AFR | 2,014 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | DZA | AFR | 2,015 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | ETH | AFR | 1,978 | 640 | null | null | 640 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | ETH | AFR | 1,979 | 686 | null | null | 686 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | TGO | AFR | 2,011 | 28 | null | null | 28 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | MDG | AFR | 2,023 | 104 | null | null | 104 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | MWI | AFR | 1,983 | 405 | null | null | 405 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | SLE | AFR | 1,983 | 713 | null | null | 713 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | CPV | AFR | 1,980 | 40 | null | null | 40 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | TZA | AFR | 2,010 | 5 | null | null | 5 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | COD | AFR | 1,981 | 802 | null | null | 802 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | BDI | AFR | 1,991 | 66 | null | null | 66 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | NGA | AFR | 1,985 | 2,679 | null | null | 2679 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | ERI | AFR | 2,008 | 8 | null | null | 8 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | SEN | AFR | 2,005 | 20 | null | null | 20 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | BDI | AFR | 1,978 | 237 | null | null | 237 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | BEN | AFR | 1,998 | 15 | null | null | 15 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | CIV | AFR | 2,022 | 630 | null | null | 630 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | SEN | AFR | 1,979 | 1,095 | null | null | 1095 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | COG | AFR | 1,984 | 144 | null | null | 144 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | BEN | AFR | 1,999 | 296 | null | null | 296 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | GNB | AFR | 2,015 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | BEN | AFR | 2,008 | 7 | null | null | 7 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | SLE | AFR | 1,989 | 188 | null | null | 188 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | ZWE | AFR | 2,005 | 9 | null | null | 9 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | STP | AFR | 1,997 | 4 | null | null | 4 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | MUS | AFR | 2,006 | 1 | null | null | 1 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | LBR | AFR | 1,974 | 85 | null | null | 85 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | MRT | AFR | 1,988 | 48 | null | null | 48 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | STP | AFR | 2,021 | 1 | null | null | 1 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | MDG | AFR | 2,020 | 111 | null | null | 111 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | UGA | AFR | 2,021 | 57 | null | null | 57 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | BFA | AFR | 1,979 | 569 | null | null | 569 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | NER | AFR | 1,980 | 346 | null | null | 346 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | COM | AFR | 2,024 | 1 | null | null | 1 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | NGA | AFR | 2,023 | 60 | null | null | 60 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | MDG | AFR | 1,996 | 77 | null | null | 77 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | DZA | AFR | 1,987 | 415 | null | null | 415 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | GAB | AFR | 1,974 | 79 | null | null | 79 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | LSO | AFR | 2,002 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | MDG | AFR | 1,978 | 637 | null | null | 637 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | KEN | AFR | 1,989 | 1,048 | null | null | 1048 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | COM | AFR | 2,017 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | GAB | AFR | 1,989 | 34 | null | null | 34 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | ETH | AFR | 1,982 | 2,724 | null | null | 2724 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | SLE | AFR | 1,982 | 739 | null | null | 739 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | MOZ | AFR | 2,022 | 105 | null | null | 105 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | MRT | AFR | 1,985 | 135 | null | null | 135 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | SWZ | AFR | 2,006 | 1 | null | null | 1 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | SLE | AFR | 2,005 | 10 | null | null | 10 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | KEN | AFR | 1,981 | 4,094 | null | null | 4094 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | SWZ | AFR | 2,020 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | LBR | AFR | 1,982 | 185 | null | null | 185 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | ETH | AFR | 1,996 | 293 | null | null | 293 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | CPV | AFR | 2,003 | 2 | null | null | 2 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | CPV | AFR | 1,989 | 8 | null | null | 8 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | CAF | AFR | 1,988 | 45 | null | null | 45 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | CAF | AFR | 2,000 | 37 | null | null | 37 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | GMB | AFR | 2,013 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | SYC | AFR | 2,022 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | ETH | AFR | 2,013 | 16 | null | null | 16 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | BFA | AFR | 2,017 | 105 | null | null | 105 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | GIN | AFR | 1,977 | 67 | null | null | 67 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | SLE | AFR | 2,015 | 11 | null | null | 11 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | CIV | AFR | 2,014 | 12 | null | null | 12 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | BFA | AFR | 2,002 | 4 | null | null | 4 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | LBR | AFR | 1,981 | 309 | null | null | 309 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | SWZ | AFR | 1,986 | 154 | null | null | 154 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | ETH | AFR | 2,019 | 107 | null | null | 107 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | GIN | AFR | 1,980 | 482 | null | null | 482 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | SSD | AFR | 2,023 | 127 | null | null | 127 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | CMR | AFR | 2,024 | 36 | null | null | 36 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | BWA | AFR | 2,001 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | GNQ | AFR | 2,010 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | COD | AFR | 1,980 | 848 | null | null | 848 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | STP | AFR | 2,001 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | BDI | AFR | 2,000 | 33 | null | null | 33 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | NER | AFR | 2,008 | 136 | null | null | 136 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | ZWE | AFR | 2,015 | 7 | null | null | 7 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | MWI | AFR | 2,005 | 6 | null | null | 6 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | GMB | AFR | 2,022 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | MLI | AFR | 2,010 | 86 | null | null | 86 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | GNQ | AFR | 2,012 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | LBR | AFR | 2,007 | 13 | null | null | 13 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | GHA | AFR | 1,984 | 600 | null | null | 600 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | DZA | AFR | 1,996 | 26 | null | null | 26 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | MWI | AFR | 2,008 | 5 | null | null | 5 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | TGO | AFR | 2,016 | 11 | null | null | 11 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | STP | AFR | 2,012 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | CAF | AFR | 1,980 | 217 | null | null | 217 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | TGO | AFR | 1,982 | 252 | null | null | 252 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | CMR | AFR | 2,000 | 279 | null | null | 279 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | KEN | AFR | 1,984 | 2,963 | null | null | 2963 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | STP | AFR | 2,011 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | SWZ | AFR | 1,989 | 25 | null | null | 25 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | COG | AFR | 1,995 | 5 | null | null | 5 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | TCD | AFR | 1,974 | 728 | null | null | 728 | 2025-07-14T16:36:19.017+02:00 |
WHS3_46 | GNB | AFR | 2,016 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
Africa — WHO GHO: Total tetanus - number of reported cases
Indicator code: WHS3_46
HuggingFace slug: electricsheepafrica/africa-who-total-tetanus-number-of-reported-cases
Source: WHO Global Health Observatory
License: CC BY 4.0 — WHO Open Data
Dataset Description
This dataset contains country-level observations for the WHO GHO indicator "Total tetanus - number of reported cases" (WHS3_46) across African nations, spanning 1974–2024. It is part of the Electric Sheep Africa collection — a unified, ML-ready repository of African data.
Data is sourced directly from the WHO Global Health Observatory OData API and repackaged as Parquet files with a consistent schema. All values are drawn from NumericValue (the float-precision field), not the display string. Confidence interval bounds (value_low, value_high) are included where available.
Coverage
| Countries | 47 African nations |
| Years | 1974 – 2024 |
| Total rows | 1,750 |
| Region filter | WHO AFRO (ParentLocationCode = 'AFR') |
Countries included: AGO, BDI, BEN, BFA, BWA, CAF, CIV, CMR, COD, COG, COM, CPV, DZA, ERI, ETH, GAB, GHA, GIN, GMB, GNB … and 27 more
Sub-dimensions
No sub-dimensions (single value per country/year)
When an indicator is stratified (e.g., by sex or age group), each unique combination of country × year × dimension produces a separate row. Filter on dim1 / dim2 for the stratum you need, or aggregate across strata.
Schema
| Column | Type | Description |
|---|---|---|
indicator_code |
string | GHO indicator code (e.g., WHS3_46) |
country_iso3 |
string | ISO 3166-1 alpha-3 country code |
who_region |
string | WHO region code (always AFR here) |
year |
int | Observation year |
value_numeric |
float | Point estimate (primary ML target) |
value_low |
float | Lower confidence bound (if available) |
value_high |
float | Upper confidence bound (if available) |
value_display |
string | Formatted display string, e.g. "58.3 [57.7–59.0]" |
dim1_type |
string | Dimension 1 type, e.g. SEX, RESIDENCEAREATYPE |
dim1 |
string | Dimension 1 value, e.g. SEX_BTSX, RURAL |
dim2_type |
string | Dimension 2 type (if present) |
dim2 |
string | Dimension 2 value (if present) |
last_updated |
string | WHO data last-updated timestamp |
Usage
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-who-total-tetanus-number-of-reported-cases")
df = ds["train"].to_pandas()
# Both-sexes, national level only
national = df[df.get("dim1", "").str.endswith("_BTSX") | df.get("dim1", pd.Series()).isna()]
# Time series for one country
kenya = df[df["country_iso3"] == "KEN"].sort_values("year")
Citation
@misc{who_gho_whs3_46,
title = {WHO Global Health Observatory: Total tetanus - number of reported cases},
author = {World Health Organization},
year = {2024},
url = {https://www.who.int/data/gho/data/indicators/indicator-details/GHO/WHS3_46},
note = {Repackaged by Electric Sheep Africa}
}
Repackaged by Electric Sheep Africa from WHO GHO open data. Original data © World Health Organization, licensed CC BY 4.0.
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