electricsheepafrica/africa-attacks-on-vaccination-campaigns

Attacks on Public Health-Related Programmes Data

Publisher: Insecurity Insight · Source: HDX · License: cc-by-sa · Updated: 2026-04-13

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Abstract

This page contains agency- and open source events affecting public-health related programmes, including COVID-19, Ebola, and vaccination campaigns. published in the Attacks on Health Care News Brief. Categorized by country. Please get in touch if you are interested in curated Resources: info@insecurityinsight.org.

Each row in this dataset represents discrete events or incidents. Temporal coverage is indicated by the date, date_event_entered column(s). Geographic scope: AFG, BGD, BRA, BFA, KHM, CAF, COD, ETH, and 23 others.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Public health
Unit of observation	Discrete events or incidents
Rows (total)	188
Columns	19 (4 numeric, 12 categorical, 3 datetime)
Train split	150 rows
Test split	37 rows
Geographic scope	AFG, BGD, BRA, BFA, KHM, CAF, COD, ETH, and 23 others
Publisher	Insecurity Insight
HDX last updated	2026-04-13

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Variables

Geographic — country (Pakistan, Afghanistan, India), country_iso (PAK, AFG, IND), admin_1 (Khyber Pakhtunkhwa, Balochistan, Punjab), location_of_incident (Health building, Project site, Road), covid_related_violence (COVID) and 1 others.

Temporal — date, date_event_entered, date_event_modified.

Outcome / Measurement — total_health_workers_killed (range 1.0–4.0), number_of_incidents_where_health_facilities_providing_public_re (range 0.0–1.0), number_of_incidents_where_health_transport_providing_public_rel (range 0.0–1.0).

Identifier / Metadata — reported_perpetrator_name (Unidentified Armed Actor, No Information, Local population), event_id (range 24162.0–125619.0), esa_source, esa_processed.

Other — geo_precision (censored), reported_perpetrator (NSA, No Information, Government: Military), weapon_carried_used (Firearms, Other Weapon, No Information on the Weapon Used).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-attacks-on-vaccination-campaigns")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
date	datetime64[ns]	0.0%
country	object	0.0%	Pakistan, Afghanistan, India
country_iso	object	0.0%	PAK, AFG, IND
admin_1	object	0.0%	Khyber Pakhtunkhwa, Balochistan, Punjab
geo_precision	object	0.0%	censored
reported_perpetrator	object	0.0%	NSA, No Information, Government: Military
reported_perpetrator_name	object	0.0%	Unidentified Armed Actor, No Information, Local population
weapon_carried_used	object	0.0%	Firearms, Other Weapon, No Information on the Weapon Used
location_of_incident	object	0.0%	Health building, Project site, Road
covid_related_violence	object	76.6%	COVID
polio_related_violence	object	42.0%	Polio
total_health_workers_killed	float64	76.6%	1.0 – 4.0 (mean 1.5909)
number_of_incidents_where_health_facilities_providing_public_re	int64	0.0%	0.0 – 1.0 (mean 0.2021)
number_of_incidents_where_health_transport_providing_public_rel	int64	0.0%	0.0 – 1.0 (mean 0.1649)
event_id	int64	0.0%	24162.0 – 125619.0 (mean 51831.9255)
date_event_entered	datetime64[ns]	0.0%
date_event_modified	datetime64[ns]	0.0%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
total_health_workers_killed	1.0	4.0	1.5909	1.0
number_of_incidents_where_health_facilities_providing_public_re	0.0	1.0	0.2021	0.0
number_of_incidents_where_health_transport_providing_public_rel	0.0	1.0	0.1649	0.0
event_id	24162.0	125619.0	51831.9255	36017.5

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Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. 11 column(s) with >80% missing values were removed: event_description, latitude, longitude, ebola_related_violence, cholera_related_violence, measles_related_violence.... The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from Insecurity Insight and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• The following columns have >20% missing values and should be treated with caution in modelling: covid_related_violence, polio_related_violence, total_health_workers_killed.
• This dataset spans 31 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_attacks_on_vaccination_campaigns,
  title     = {Attacks on Public Health-Related Programmes Data},
  author    = {Insecurity Insight},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/attacks-on-vaccination-campaigns},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.