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 137k | value_low null | value_high null | value_display stringlengths 1 7 | last_updated stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|
WHS3_43 | UGA | AFR | 2,022 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | CMR | AFR | 1,988 | 5,659 | null | null | 5659 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | SYC | AFR | 2,009 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | MOZ | AFR | 2,003 | 13 | null | null | 13 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | LBR | AFR | 1,988 | 4,561 | null | null | 4561 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | CAF | AFR | 2,012 | 124 | null | null | 124 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | GMB | AFR | 2,006 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | SWZ | AFR | 2,015 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | ZMB | AFR | 2,004 | 180 | null | null | 180 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | ERI | AFR | 2,002 | 149 | null | null | 149 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | NER | AFR | 2,005 | 1,206 | null | null | 1206 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | CAF | AFR | 1,977 | 1,845 | null | null | 1845 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | CPV | AFR | 2,018 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | SYC | AFR | 2,006 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | DZA | AFR | 2,004 | 7 | null | null | 7 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | BDI | AFR | 1,993 | 95 | null | null | 95 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | COG | AFR | 2,023 | 1 | null | null | 1 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | GHA | AFR | 1,995 | 1,147 | null | null | 1147 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | ERI | AFR | 1,998 | 119 | null | null | 119 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | LSO | AFR | 1,981 | 416 | null | null | 416 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | SWZ | AFR | 2,001 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | UGA | AFR | 1,982 | 1,376 | null | null | 1376 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | STP | AFR | 2,010 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | NAM | AFR | 2,012 | 2 | null | null | 2 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | MOZ | AFR | 1,990 | 451 | null | null | 451 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | COD | AFR | 1,981 | 13,731 | null | null | 13 731 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | TGO | AFR | 2,013 | 30 | null | null | 30 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | SEN | AFR | 2,009 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | MLI | AFR | 2,019 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | LBR | AFR | 1,986 | 15 | null | null | 15 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | CAF | AFR | 2,009 | 63 | null | null | 63 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | MWI | AFR | 2,003 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | BWA | AFR | 2,014 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | KEN | AFR | 1,974 | 12,156 | null | null | 12 156 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | SWZ | AFR | 2,022 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | BEN | AFR | 1,982 | 2,705 | null | null | 2705 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | ZAF | AFR | 1,998 | 39 | null | null | 39 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | CPV | AFR | 2,008 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | SEN | AFR | 2,015 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | BFA | AFR | 1,976 | 5,810 | null | null | 5810 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | SYC | AFR | 2,001 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | SYC | AFR | 1,998 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | SYC | AFR | 1,986 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | RWA | AFR | 1,980 | 16,189 | null | null | 16 189 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | SYC | AFR | 1,984 | 1 | null | null | 1 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | STP | AFR | 2,000 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | GNQ | AFR | 1,986 | 15 | null | null | 15 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | COD | AFR | 2,008 | 3,190 | null | null | 3190 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | GMB | AFR | 2,003 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | TZA | AFR | 2,007 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | GHA | AFR | 2,014 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | ZWE | AFR | 2,003 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | RWA | AFR | 1,996 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | NAM | AFR | 2,001 | 45 | null | null | 45 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | CPV | AFR | 1,998 | 47 | null | null | 47 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | ZWE | AFR | 2,012 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | RWA | AFR | 2,023 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | STP | AFR | 1,986 | 96 | null | null | 96 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | LBR | AFR | 1,979 | 100 | null | null | 100 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | ZMB | AFR | 1,990 | 200 | null | null | 200 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | MLI | AFR | 1,974 | 7,851 | null | null | 7851 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | GHA | AFR | 2,022 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | NER | AFR | 2,021 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | GHA | AFR | 2,017 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | KEN | AFR | 1,988 | 422 | null | null | 422 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | TGO | AFR | 2,015 | 23 | null | null | 23 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | MDG | AFR | 1,983 | 20,891 | null | null | 20 891 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | COG | AFR | 1,975 | 4,971 | null | null | 4971 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | CIV | AFR | 1,989 | 4,284 | null | null | 4284 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | COD | AFR | 2,021 | 1,494 | null | null | 1494 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | NGA | AFR | 2,015 | 6,592 | null | null | 6592 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | LSO | AFR | 1,987 | 36 | null | null | 36 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | MUS | AFR | 2,010 | 1 | null | null | 1 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | MRT | AFR | 1,990 | 383 | null | null | 383 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | UGA | AFR | 1,981 | 4,196 | null | null | 4196 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | MRT | AFR | 1,976 | 4,275 | null | null | 4275 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | SYC | AFR | 1,997 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | GNB | AFR | 2,002 | 4 | null | null | 4 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | BEN | AFR | 1,987 | 2,148 | null | null | 2148 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | NER | AFR | 2,023 | 1,190 | null | null | 1190 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | ZAF | AFR | 2,022 | 789 | null | null | 789 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | COG | AFR | 2,020 | 34 | null | null | 34 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | CIV | AFR | 2,019 | 174 | null | null | 174 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | COD | AFR | 1,976 | 18,975 | null | null | 18 975 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | ZWE | AFR | 2,008 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | COG | AFR | 2,012 | 12 | null | null | 12 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | NAM | AFR | 1,997 | 221 | null | null | 221 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | SWZ | AFR | 1,992 | 34 | null | null | 34 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | MLI | AFR | 1,982 | 3,003 | null | null | 3003 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | GHA | AFR | 2,003 | 105 | null | null | 105 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | ERI | AFR | 2,010 | 11 | null | null | 11 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | LSO | AFR | 1,988 | 48 | null | null | 48 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | CAF | AFR | 1,990 | 46 | null | null | 46 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | BFA | AFR | 1,978 | 2,747 | null | null | 2747 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | GIN | AFR | 2,008 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | SWZ | AFR | 1,983 | 1,294 | null | null | 1294 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | BWA | AFR | 1,990 | 22 | null | null | 22 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | ERI | AFR | 2,006 | 65 | null | null | 65 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | GHA | AFR | 2,008 | 4 | null | null | 4 | 2025-07-14T16:36:19.017+02:00 |
WHS3_43 | CPV | AFR | 2,019 | 0 | null | null | 0 | 2025-07-14T16:36:19.017+02:00 |
Africa — WHO GHO: Pertussis - number of reported cases
Indicator code: WHS3_43
HuggingFace slug: electricsheepafrica/africa-who-pertussis-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 "Pertussis - number of reported cases" (WHS3_43) 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 | 1,574 |
| 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_43) |
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-pertussis-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_43,
title = {WHO Global Health Observatory: Pertussis - number of reported cases},
author = {World Health Organization},
year = {2024},
url = {https://www.who.int/data/gho/data/indicators/indicator-details/GHO/WHS3_43},
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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