electricsheepafrica/africa-long-covidresearchagenda
收藏Hugging Face2026-04-09 更新2026-04-12 收录
下载链接：
https://hf-mirror.com/datasets/electricsheepafrica/africa-long-covidresearchagenda
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资源简介：
---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- covid-19
- health
- health-facilities
- logistics
- ken
- mwi
pretty_name: "Kenya, Malawi, Long Covid-19 effects survey dataset"
dataset_info:
splits:
- name: train
num_examples: 644
- name: test
num_examples: 161
---
# Kenya, Malawi, Long Covid-19 effects survey dataset
**Publisher:** Wellcome (inactive) · **Source:** [HDX](https://data.humdata.org/dataset/long-covidresearchagenda) · **License:** `cc-by-sa` · **Updated:** 2024-05-10
---
## Abstract
Carried out in Kenya and Malawi, this work sought to better understand how Long COVID affects those living with the condition, their carers and the health care professionals treating it. The result is a research agenda outlining a list of priorities to inform the direction of future research.
Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the `start_time`, `end_time` column(s). Geographic scope: **KEN, MWI**.
*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
---
## Dataset Characteristics
| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 806 |
| **Columns** | 668 (3 numeric, 663 categorical, 2 datetime) |
| **Train split** | 644 rows |
| **Test split** | 161 rows |
| **Geographic scope** | KEN, MWI |
| **Publisher** | Wellcome (inactive) |
| **HDX last updated** | 2024-05-10 |
---
## Variables
**Geographic** — `ea_type` (Urban, Rural), `body_organ_disease`, `respiratory_condition`, `country`, `symptoms` and 41 others.
**Temporal** — `start_time`, `end_time`.
**Demographic** — `gender` (Male, Female), `age` (18-33, 34-50, >50).
**Identifier / Metadata** — `instanceid` (uuid:a3dc17ef-9135-4ff4-ad5e-2451fda3b672, uuid:d6bdb6ec-8428-4f31-b221-a36b685abe52, uuid:fb4f0935-193d-4a13-84dc-dd5bfd8843aa), `esa_source`, `esa_processed`.
**Other** — `education` (Secondary, Tertiary, Primary), `religion` (Christian, other religion, no religion), `marital_stat` (Married, No partner, Co-habiting), `occupation` (Employed, Unemployed), `no_occupants` (1 to 3, 4 or more, 0) and 610 others.
---
## Quick Start
```python
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-long-covidresearchagenda")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()
print(train.shape)
train.head()
```
---
## Schema
| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `instanceid` | object | 0.0% | uuid:a3dc17ef-9135-4ff4-ad5e-2451fda3b672, uuid:d6bdb6ec-8428-4f31-b221-a36b685abe52, uuid:fb4f0935-193d-4a13-84dc-dd5bfd8843aa |
| `ea_type` | object | 0.0% | Urban, Rural |
| `start_time` | datetime64[ns] | 0.0% | |
| `gender` | object | 0.6% | Male, Female |
| `age` | object | 2.5% | 18-33, 34-50, >50 |
| `education` | object | 58.4% | Secondary, Tertiary, Primary |
| `religion` | object | 5.0% | Christian, other religion, no religion |
| `marital_stat` | object | 30.1% | Married, No partner, Co-habiting |
| `occupation` | object | 8.3% | Employed, Unemployed |
| `no_occupants` | object | 18.5% | 1 to 3, 4 or more, 0 |
| `communicable_disease` | object | 2.4% | No |
| `non_communicable_disease` | object | 0.5% | |
| `body_organ_disease` | object | 0.9% | |
| `neurological_condition` | object | 0.1% | |
| `respiratory_condition` | object | 2.6% | |
| `other_disease` | object | 1.1% | |
| `country` | object | 0.0% | |
| `symptoms` | object | 0.1% | |
| `symptoms_fatigue` | object | 0.0% | |
| `symptoms_headache` | object | 0.0% | |
| `symptoms_chest_pain` | object | 0.0% | |
| `symptoms_shortness_of_breath` | object | 0.0% | |
| `symptoms_diarrhea` | object | 0.0% | |
| `symptoms_abdominal_pain` | object | 0.0% | |
| `symptoms_joint_pain` | object | 0.0% | |
| `symptoms_vomiting` | object | 0.0% | |
| `symptoms_muscle_pain_and_needles` | object | 0.0% | |
| `symptoms_loss_of_smell` | object | 0.0% | |
| `symptoms_loss_of_taste` | object | 0.0% | |
| `symptoms_persistent_cough` | object | 0.0% | |
| `symptoms_palpitations` | object | 0.0% | |
| `symptoms_rash` | object | 0.0% | |
| `symptoms_recurrent_fever` | object | 0.0% | |
| `symptoms_cough` | object | 0.0% | |
| `symptoms_post_exertion_malaise` | object | 0.0% | |
| `symptoms_tinnitus_buzzing_in_ears` | object | 0.0% | |
| `symptoms_nerve_pain_burning` | object | 0.0% | |
| `symptoms_sharp_pain_in_the_ribs` | object | 0.0% | |
| `symptoms_sore_throat` | object | 0.0% | |
| `symptoms_acid_reflux` | object | 0.0% | |
| `symptoms_tiredness` | object | 0.0% | |
| `symptoms_sneezing` | object | 0.0% | |
| `symptoms_running_nose` | object | 0.0% | |
| `symptoms_redding_of_eye` | object | 0.0% | |
| `symptoms_recurring_cold` | object | 0.0% | |
| `symptoms_nose_bleed` | object | 0.0% | |
| `symptoms_nose_blockage` | object | 0.0% | |
| `symptoms_nausea` | object | 0.0% | |
| `symptoms_loss_of_appetite` | object | 0.0% | |
| `symptoms_none` | object | 0.0% | |
| `symptoms_flu` | object | 0.0% | |
| `symptoms_dizziness` | object | 0.0% | |
| `symptoms_blurred_vision` | object | 0.0% | |
| `symptoms_chest_congestion` | object | 0.0% | |
| `symptoms_tonsils` | object | 0.0% | |
| `symptoms_back_ache` | object | 0.0% | |
| `c1_1a` | float64 | 63.0% | 2012.0 – 2021.0 (mean 2020.8389) |
| `c1_2a` | object | 63.0% | |
| `c1_4a` | object | 63.0% | |
| `c1_1b` | float64 | 48.5% | 2010.0 – 2021.0 (mean 2020.8337) |
| `c1_2b` | object | 48.5% | |
| `c1_3b` | object | 70.5% | |
| `c1_4b` | object | 48.6% | |
| `c1_1c` | float64 | 71.8% | 2000.0 – 2021.0 (mean 2020.6079) |
| `c1_2c` | object | 71.8% | |
| `c1_4c` | object | 72.0% | |
| `c2` | object | 0.0% | |
| `c2_1` | object | 0.0% | |
| `c2_2` | object | 0.0% | |
| `c2_3` | object | 0.0% | |
| `c2_4` | object | 0.0% | |
| `c2_5` | object | 0.0% | |
| `c2_6` | object | 0.0% | |
| `c2_7` | object | 0.0% | |
| `c2_8` | object | 0.0% | |
| `c2_9` | object | 0.0% | |
| `c3_1_1` | object | 63.0% | |
| `c3_1_2` | object | 48.5% | |
| `c3_1_3` | object | 71.8% | |
| `c3_1_10` | object | 73.0% | |
| `c3_1_11` | object | 72.5% | |
| `c3_1_12` | object | 79.9% | |
| `c3_1_15` | object | 76.1% | |
| `c3_1_16` | object | 78.4% | |
| `c3b_1_1` | object | 71.2% | |
| `c3b_1_4` | object | 63.8% | |
| `c3b_1_7` | object | 64.0% | |
| `c3_2_1` | object | 63.0% | |
| `c3_2_2` | object | 48.5% | |
| `c3_2_3` | object | 71.8% | |
| `c3_2_10` | object | 73.0% | |
| `c3_2_11` | object | 72.5% | |
| `c3_2_15` | object | 76.1% | |
| `c3_2_16` | object | 78.4% | |
| `c3b_2_1` | object | 71.2% | |
| `c3b_2_4` | object | 63.8% | |
| `c3b_2_7` | object | 64.0% | |
| `c3_3_1` | object | 63.0% | |
| `c3_3_2` | object | 48.5% | |
| `c3_3_3` | object | 71.8% | |
| `c3_3_10` | object | 73.0% | |
| `c3_3_11` | object | 72.5% | |
| `c3_3_15` | object | 76.1% | |
| `c3_3_16` | object | 78.4% | |
| `c3b_3_1` | object | 71.2% | |
| `c3b_3_4` | object | 63.8% | |
| `c3b_3_7` | object | 64.0% | |
| `c4` | object | 0.0% | |
| `c4_1` | object | 0.0% | |
| `c4_2` | object | 0.0% | |
| `c4_3` | object | 0.0% | |
| `c4_4` | object | 0.0% | |
| `c4_5` | object | 0.0% | |
| `c4_6` | object | 0.0% | |
| `c4_7` | object | 0.0% | |
| `c4_8` | object | 0.0% | |
| `c4_9` | object | 0.0% | |
| `c4_10` | object | 0.0% | |
| `c4_11` | object | 0.0% | |
| `c4_12` | object | 0.0% | |
| `c4_13` | object | 0.0% | |
| `c4_14` | object | 0.0% | |
| `c4_15` | object | 0.0% | |
| `c4_16` | object | 0.0% | |
| `c4_17` | object | 0.0% | |
| `c4_18` | object | 0.0% | |
| `c4_19` | object | 0.0% | |
| `c4_20` | object | 0.0% | |
| `c4_21` | object | 0.0% | |
| `c4_22` | object | 0.0% | |
| `c4_23` | object | 0.0% | |
| `c4_24` | object | 0.0% | |
| `c4_25` | object | 0.0% | |
| `c4_26` | object | 0.0% | |
| `c4_27` | object | 0.0% | |
| `c4_28` | object | 0.0% | |
| `c4_29` | object | 0.0% | |
| `c4_30` | object | 0.0% | |
| `c4_31` | object | 0.0% | |
| `c4_32` | object | 0.0% | |
| `c4_33` | object | 0.0% | |
| `c4_34` | object | 0.0% | |
| `c4_35` | object | 0.0% | |
| `c4_36` | object | 0.0% | |
| `c4_37` | object | 0.0% | |
| `c4_38` | object | 0.0% | |
| `c4b` | object | 23.2% | |
| `c4b_1` | object | 23.2% | |
| `c4b_2` | object | 23.2% | |
| `c4b_3` | object | 23.2% | |
| `c4b_4` | object | 23.2% | |
| `c4b_5` | object | 23.2% | |
| `c4b_6` | object | 23.2% | |
| `c4b_7` | object | 23.2% | |
| `c4b_8` | object | 23.2% | |
| `c4b_9` | object | 23.2% | |
| `c6` | object | 0.0% | |
| `c6b` | object | 23.2% | |
| `c7` | object | 0.0% | |
| `c8` | object | 24.2% | |
| `c8_1` | object | 24.2% | |
| `c8_2` | object | 24.2% | |
| `c8_3` | object | 24.2% | |
| `c8_4` | object | 24.2% | |
| `c8_5` | object | 24.2% | |
| `c8_6` | object | 24.2% | |
| `c8_7` | object | 24.2% | |
| `c8_8` | object | 24.2% | |
| `c8_9` | object | 24.2% | |
| `c9` | object | 69.9% | |
| `c9_1` | object | 69.9% | |
| `c9_2` | object | 69.9% | |
| `c9_3` | object | 69.9% | |
| `c9_4` | object | 69.9% | |
| `c9_5` | object | 69.9% | |
| `c10` | object | 0.0% | |
| `c10_1` | object | 0.0% | |
| `c10_2` | object | 0.0% | |
| `c10_3` | object | 0.0% | |
| `c10_4` | object | 0.0% | |
| `c10_5` | object | 0.0% | |
| `c10_6` | object | 0.0% | |
| `c10_7` | object | 0.0% | |
| `c10_8` | object | 0.0% | |
| `c10_9` | object | 0.0% | |
| `c10_10` | object | 0.0% | |
| `c10_11` | object | 0.0% | |
| `c10_12` | object | 0.0% | |
| `c10_13` | object | 0.0% | |
| `c10_14` | object | 0.0% | |
| `c10_15` | object | 0.0% | |
| `c10_16` | object | 0.0% | |
| `c10_17` | object | 0.0% | |
| `c10_18` | object | 0.0% | |
| `c10_19` | object | 0.0% | |
| `c10_20` | object | 0.0% | |
| `c10_21` | object | 0.0% | |
| `c10_22` | object | 0.0% | |
| `c10_23` | object | 0.0% | |
| `c10_26` | object | 0.0% | |
| `c10_24` | object | 0.0% | |
| `c10_25` | object | 0.0% | |
| `c10_27` | object | 0.0% | |
| `c10_28` | object | 0.0% | |
| `c10_29` | object | 0.0% | |
| `c10_30` | object | 0.0% | |
| `c10_31` | object | 0.0% | |
| `c10_32` | object | 0.0% | |
| `c10_33` | object | 0.0% | |
| `c10_34` | object | 0.0% | |
| `c10_35` | object | 0.0% | |
| `c10_36` | object | 0.0% | |
| `c10_1_1` | object | 72.1% | |
| `c10_1_2` | object | 72.1% | |
| `c10_1_3` | object | 72.1% | |
| `c10_1_4` | object | 72.1% | |
| `c10_1_6` | object | 72.1% | |
| `c10_1_7` | object | 72.1% | |
| `c10_1_8` | object | 72.1% | |
| `c12a` | object | 63.8% | |
| `c12a_1` | object | 63.8% | |
| `c12a_2` | object | 63.8% | |
| `c12a_3` | object | 63.8% | |
| `c12a_4` | object | 63.8% | |
| `c12a_5` | object | 63.8% | |
| `c12a_6` | object | 63.8% | |
| `c12a_7` | object | 63.8% | |
| `c12a_8` | object | 63.8% | |
| `c12a_9` | object | 63.8% | |
| `c12a_10` | object | 63.8% | |
| `c12a_12` | object | 63.8% | |
| `c12a_13` | object | 63.8% | |
| `c12a_14` | object | 63.8% | |
| `c12a_15` | object | 63.8% | |
| `c12a_16` | object | 63.8% | |
| `c12a_17` | object | 63.8% | |
| `c12a_18` | object | 63.8% | |
| `c12a_19` | object | 63.8% | |
| `c12a_20` | object | 63.8% | |
| `c12a_21` | object | 63.8% | |
| `c12a_22` | object | 63.8% | |
| `c12a_23` | object | 63.8% | |
| `c16_1` | object | 2.9% | |
| `c16_1_1` | object | 2.9% | |
| `c16_2_1` | object | 2.9% | |
| `c16_3_1` | object | 2.9% | |
| `c16_4_1` | object | 2.9% | |
| `c16_5_1` | object | 2.9% | |
| `c16_6_1` | object | 2.9% | |
| `c16_7_1` | object | 2.9% | |
| `c16_8_1` | object | 2.9% | |
| `c16_9_1` | object | 2.9% | |
| `c16_10_1` | object | 2.9% | |
| `c16_11_1` | object | 2.9% | |
| `c13_1` | object | 2.9% | |
| `c13_1_1` | object | 2.9% | |
| `c13_2_1` | object | 2.9% | |
| `c13_3_1` | object | 2.9% | |
| `c13_4_1` | object | 2.9% | |
| `c13_5_1` | object | 2.9% | |
| `c13_6_1` | object | 2.9% | |
| `c13_7_1` | object | 2.9% | |
| `v2` | object | 14.8% | |
| `c13_1_1_1` | object | 14.8% | |
| `c13_1_2_1` | object | 14.8% | |
| `c13_1_3_1` | object | 14.8% | |
| `c13_1_4_1` | object | 14.8% | |
| `c13_1_5_1` | object | 14.8% | |
| `c13_1_6_1` | object | 14.8% | |
| `c13_1_7_1` | object | 14.8% | |
| `c13_1_8_1` | object | 14.8% | |
| `c13_1_9_1` | object | 14.8% | |
| `c13_1_10_1` | object | 14.8% | |
| `c13_1_11_1` | object | 14.8% | |
| `c13_1_12_1` | object | 14.8% | |
| `c13_1_13_1` | object | 14.8% | |
| `c13_1_14_1` | object | 14.8% | |
| `c13_1_15_1` | object | 14.8% | |
| `c13_1_16_1` | object | 14.8% | |
| `c13_1_17_1` | object | 14.8% | |
| `c13_1_18_1` | object | 14.8% | |
| `c13_1_19_1` | object | 14.8% | |
| `c13_1_20_1` | object | 14.8% | |
| `c13_1_21_1` | object | 14.8% | |
| `c13_1_22_1` | object | 14.8% | |
| `c13_1_23_1` | object | 14.8% | |
| `v3` | object | 17.9% | |
| `c13_2_1_1` | object | 14.8% | |
| `c13_2_2_1` | object | 14.8% | |
| `c13_2_3_1` | object | 14.8% | |
| `c13_2_4_1` | object | 14.8% | |
| `c13_2_5_1` | object | 14.8% | |
| `c13_2_6_1` | object | 14.8% | |
| `c13_2_7_1` | object | 14.8% | |
| `c13_2_8_1` | object | 14.8% | |
| `c13_2_9_1` | object | 14.8% | |
| `c14_1_1` | object | 63.4% | |
| `c14_2_1` | object | 49.5% | |
| `c14_3_1` | object | 72.0% | |
| `c14_10_1` | object | 73.9% | |
| `c14_11_1` | object | 73.6% | |
| `c14_15_1` | object | 76.1% | |
| `c14_16_1` | object | 79.4% | |
| `c14_1b_1` | object | 64.6% | |
| `c14_4b_1` | object | 64.5% | |
| `c14_8b_1` | object | 71.5% | |
| `c15_1` | object | 2.9% | |
| `c20_1` | object | 53.8% | |
| `c20_1_1` | object | 53.8% | |
| `c20_2_1` | object | 53.8% | |
| `c20_3_1` | object | 53.8% | |
| `c20_4_1` | object | 53.8% | |
| `c20_5_1` | object | 53.8% | |
| `c20_6_1` | object | 53.8% | |
| `c20_7_1` | object | 53.8% | |
| `c20_8_1` | object | 53.8% | |
| `c20_9_1` | object | 53.8% | |
| `c20_10_1` | object | 53.8% | |
| `c20_11_1` | object | 53.8% | |
| `c20_12_1` | object | 53.8% | |
| `c20_13_1` | object | 53.8% | |
| `c20_14_1` | object | 53.8% | |
| `c20_15_1` | object | 53.8% | |
| `c20_16_1` | object | 53.8% | |
| `c20_17_1` | object | 53.8% | |
| `c20_18_1` | object | 53.8% | |
| `c16_2` | object | 28.3% | |
| `c16_1_2` | object | 28.3% | |
| `c16_2_2` | object | 28.3% | |
| `c16_3_2` | object | 28.3% | |
| `c16_4_2` | object | 28.3% | |
| `c16_5_2` | object | 28.3% | |
| `c16_6_2` | object | 28.3% | |
| `c16_7_2` | object | 28.3% | |
| `c16_8_2` | object | 28.3% | |
| `c16_9_2` | object | 28.3% | |
| `c16_10_2` | object | 28.3% | |
| `c16_11_2` | object | 28.3% | |
| `c13_2` | object | 28.3% | |
| `c13_1_2` | object | 28.3% | |
| `c13_2_2` | object | 28.3% | |
| `c13_3_2` | object | 28.3% | |
| `c13_4_2` | object | 28.3% | |
| `c13_5_2` | object | 28.3% | |
| `c13_6_2` | object | 28.3% | |
| `v4` | object | 51.0% | |
| `c13_1_1_2` | object | 51.0% | |
| `c13_1_2_2` | object | 51.0% | |
| `c13_1_3_2` | object | 51.0% | |
| `c13_1_4_2` | object | 51.0% | |
| `c13_1_5_2` | object | 51.0% | |
| `c13_1_6_2` | object | 51.0% | |
| `c13_1_7_2` | object | 51.0% | |
| `c13_1_8_2` | object | 51.0% | |
| `c13_1_9_2` | object | 51.0% | |
| `c13_1_10_2` | object | 51.0% | |
| `c13_1_11_2` | object | 51.0% | |
| `c13_1_12_2` | object | 51.0% | |
| `c13_1_13_2` | object | 51.0% | |
| `c13_1_14_2` | object | 51.0% | |
| `c13_1_15_2` | object | 51.0% | |
| `c13_1_16_2` | object | 51.0% | |
| `c13_1_17_2` | object | 51.0% | |
| `c13_1_18_2` | object | 51.0% | |
| `c13_1_19_2` | object | 51.0% | |
| `c13_1_20_2` | object | 51.0% | |
| `c13_1_21_2` | object | 51.0% | |
| `c13_1_22_2` | object | 51.0% | |
| `c13_1_23_2` | object | 51.0% | |
| `v5` | object | 51.0% | |
| `c13_2_1_2` | object | 51.0% | |
| `c13_2_2_2` | object | 51.0% | |
| `c13_2_3_2` | object | 51.0% | |
| `c13_2_4_2` | object | 51.0% | |
| `c13_2_5_2` | object | 51.0% | |
| `c13_2_6_2` | object | 51.0% | |
| `c13_2_7_2` | object | 51.0% | |
| `c13_2_8_2` | object | 51.0% | |
| `c13_2_9_2` | object | 51.0% | |
| `c14_1_2` | object | 69.2% | |
| `c14_2_2` | object | 60.4% | |
| `c14_3_2` | object | 75.9% | |
| `c14_10_2` | object | 79.5% | |
| `c14_11_2` | object | 79.9% | |
| `c14_1b_2` | object | 72.5% | |
| `c14_4b_2` | object | 71.3% | |
| `c14_8b_2` | object | 76.2% | |
| `c15_2` | object | 28.2% | |
| `c20_2` | object | 72.0% | |
| `c20_1_2` | object | 72.0% | |
| `c20_2_2` | object | 72.0% | |
| `c20_3_2` | object | 72.0% | |
| `c20_4_2` | object | 72.0% | |
| `c20_5_2` | object | 72.0% | |
| `c20_6_2` | object | 72.0% | |
| `c20_7_2` | object | 72.0% | |
| `c20_8_2` | object | 72.0% | |
| `c20_9_2` | object | 72.0% | |
| `c20_10_2` | object | 72.0% | |
| `c20_11_2` | object | 72.0% | |
| `c20_12_2` | object | 72.0% | |
| `c20_13_2` | object | 72.0% | |
| `c20_14_2` | object | 72.0% | |
| `c20_15_2` | object | 72.0% | |
| `c20_16_2` | object | 72.0% | |
| `c20_17_2` | object | 72.0% | |
| `c20_18_2` | object | 72.0% | |
| `c20_19_2` | object | 72.0% | |
| `c16_3` | object | 59.8% | |
| `c16_1_3` | object | 59.8% | |
| `c16_2_3` | object | 59.8% | |
| `c16_3_3` | object | 59.8% | |
| `c16_4_3` | object | 59.8% | |
| `c16_5_3` | object | 59.8% | |
| `c16_6_3` | object | 59.8% | |
| `c16_7_3` | object | 59.8% | |
| `c16_8_3` | object | 59.8% | |
| `c16_9_3` | object | 59.8% | |
| `c16_10_3` | object | 59.8% | |
| `c16_11_3` | object | 59.8% | |
| `c13_3` | object | 59.8% | |
| `c13_1_3` | object | 59.8% | |
| `c13_2_3` | object | 59.8% | |
| `c13_3_3` | object | 59.8% | |
| `c13_4_3` | object | 59.8% | |
| `c13_5_3` | object | 59.8% | |
| `c13_6_3` | object | 59.8% | |
| `v6` | object | 75.3% | |
| `c13_1_1_3` | object | 75.3% | |
| `c13_1_2_3` | object | 75.3% | |
| `c13_1_3_3` | object | 75.3% | |
| `c13_1_4_3` | object | 75.3% | |
| `c13_1_5_3` | object | 75.3% | |
| `c13_1_6_3` | object | 75.3% | |
| `c13_1_7_3` | object | 75.3% | |
| `c13_1_8_3` | object | 75.3% | |
| `c13_1_9_3` | object | 75.3% | |
| `c13_1_10_3` | object | 75.3% | |
| `c13_1_11_3` | object | 75.3% | |
| `c13_1_12_3` | object | 75.3% | |
| `c13_1_13_3` | object | 75.3% | |
| `c13_1_14_3` | object | 75.3% | |
| `c13_1_15_3` | object | 75.3% | |
| `c13_1_16_3` | object | 75.3% | |
| `c13_1_17_3` | object | 75.3% | |
| `c13_1_18_3` | object | 75.3% | |
| `c13_1_19_3` | object | 75.3% | |
| `c13_1_20_3` | object | 75.3% | |
| `c13_1_21_3` | object | 75.3% | |
| `c13_1_22_3` | object | 75.3% | |
| `c13_1_23_3` | object | 75.3% | |
| `v7` | object | 75.7% | |
| `c13_2_1_3` | object | 75.3% | |
| `c13_2_2_3` | object | 75.3% | |
| `c13_2_3_3` | object | 75.3% | |
| `c13_2_4_3` | object | 75.3% | |
| `c13_2_5_3` | object | 75.3% | |
| `c13_2_6_3` | object | 75.3% | |
| `c13_2_7_3` | object | 75.3% | |
| `c13_2_8_3` | object | 75.3% | |
| `c13_2_9_3` | object | 75.3% | |
| `c14_1_3` | object | 79.9% | |
| `c14_2_3` | object | 75.4% | |
| `c15_3` | object | 59.8% | |
| `c16_4` | object | 59.8% | |
| `c11` | object | 0.0% | |
| `c11_1` | object | 0.0% | |
| `c11_2` | object | 0.0% | |
| `c11_3` | object | 0.0% | |
| `c11_4` | object | 0.0% | |
| `c11_5` | object | 0.0% | |
| `c11_6` | object | 0.0% | |
| `c11_7` | object | 0.0% | |
| `c11_8` | object | 0.0% | |
| `c11_9` | object | 0.0% | |
| `c11_10` | object | 0.0% | |
| `c11_11` | object | 0.0% | |
| `c11_12` | object | 0.0% | |
| `c11_14` | object | 0.0% | |
| `c11_15` | object | 0.0% | |
| `c11_16` | object | 0.0% | |
| `c11_17` | object | 0.0% | |
| `c11_18` | object | 0.0% | |
| `c11_19` | object | 0.0% | |
| `c11_20` | object | 0.0% | |
| `c11_21` | object | 0.0% | |
| `c11_22` | object | 0.0% | |
| `c11_23` | object | 0.0% | |
| `c11_24` | object | 0.0% | |
| `c11_25` | object | 0.0% | |
| `c11_26` | object | 0.0% | |
| `c11_27` | object | 0.0% | |
| `c11_28` | object | 0.0% | |
| `c11_29` | object | 0.0% | |
| `c11_30` | object | 0.0% | |
| `c12b` | object | 61.8% | |
| `c12b_1` | object | 61.8% | |
| `c12b_2` | object | 61.8% | |
| `c12b_3` | object | 61.8% | |
| `c12b_4` | object | 61.8% | |
| `c12b_5` | object | 61.8% | |
| `c12b_6` | object | 61.8% | |
| `c12b_7` | object | 61.8% | |
| `c12b_8` | object | 61.8% | |
| `c12b_9` | object | 61.8% | |
| `c12b_10` | object | 61.8% | |
| `c12b_11` | object | 61.8% | |
| `c12b_12` | object | 61.8% | |
| `c12b_13` | object | 61.8% | |
| `c12b_14` | object | 61.8% | |
| `c12b_15` | object | 61.8% | |
| `c12b_16` | object | 61.8% | |
| `c12b_17` | object | 61.8% | |
| `c12b_18` | object | 61.8% | |
| `c12b_19` | object | 61.8% | |
| `c12b_20` | object | 61.8% | |
| `c12b_21` | object | 61.8% | |
| `c12b_22` | object | 61.8% | |
| `c12b_23` | object | 61.8% | |
| `c12b_24` | object | 61.8% | |
| `v18` | object | 21.1% | |
| `c16_1_1_1` | object | 21.1% | |
| `c16_1_2_1` | object | 21.1% | |
| `c16_1_3_1` | object | 21.1% | |
| `c16_1_4_1` | object | 21.1% | |
| `c16_1_5_1` | object | 21.1% | |
| `c16_1_6_1` | object | 21.1% | |
| `c16_1_7_1` | object | 21.1% | |
| `c16_1_8_1` | object | 21.1% | |
| `c16_1_9_1` | object | 21.1% | |
| `c16_1_10_1` | object | 21.1% | |
| `c16_1_11_1` | object | 21.1% | |
| `c13_1b_1` | object | 21.1% | |
| `c13_1b_1_1` | object | 21.1% | |
| `c13_1b_2_1` | object | 21.1% | |
| `c13_1b_3_1` | object | 21.1% | |
| `c13_1b_4_1` | object | 21.1% | |
| `c13_1b_5_1` | object | 21.1% | |
| `c13_1b_6_1` | object | 21.1% | |
| `c13_1b_7_1` | object | 21.1% | |
| `c13_1b_8_1` | object | 21.1% | |
| `v19` | object | 69.9% | |
| `c13_1_1_1_1` | object | 69.9% | |
| `c13_1_1_2_1` | object | 69.9% | |
| `c13_1_1_3_1` | object | 69.9% | |
| `c13_1_1_4_1` | object | 69.9% | |
| `c13_1_1_5_1` | object | 69.9% | |
| `c13_1_1_6_1` | object | 69.9% | |
| `c13_1_1_7_1` | object | 69.9% | |
| `c13_1_1_8_1` | object | 69.9% | |
| `c13_1_1_9_1` | object | 69.9% | |
| `c13_1_1_10_1` | object | 69.9% | |
| `c13_1_1_11_1` | object | 69.9% | |
| `c13_1_1_12_1` | object | 69.9% | |
| `c13_1_1_13_1` | object | 69.9% | |
| `c13_1_1_14_1` | object | 69.9% | |
| `c13_1_1_15_1` | object | 69.9% | |
| `c13_1_1_16_1` | object | 69.9% | |
| `c13_1_1_17_1` | object | 69.9% | |
| `c13_1_1_18_1` | object | 69.9% | |
| `c13_1_1_19_1` | object | 69.9% | |
| `c13_1_1_20_1` | object | 69.9% | |
| `c13_1_1_21_1` | object | 69.9% | |
| `c13_1_1_22_1` | object | 69.9% | |
| `c13_1_1_23_1` | object | 69.9% | |
| `v20` | object | 69.9% | |
| `c13_1_2_1_1` | object | 69.9% | |
| `c13_1_2_2_1` | object | 69.9% | |
| `c13_1_2_3_1` | object | 69.9% | |
| `c13_1_2_4_1` | object | 69.9% | |
| `c13_1_2_5_1` | object | 69.9% | |
| `c13_1_2_6_1` | object | 69.9% | |
| `c13_1_2_7_1` | object | 69.9% | |
| `c13_1_2_8_1` | object | 69.9% | |
| `c13_1_2_9_1` | object | 69.9% | |
| `c14_1_1_1` | object | 68.1% | |
| `c14_2_1_1` | object | 57.9% | |
| `c14_3_1_1` | object | 75.8% | |
| `c14_10_1_1` | object | 78.2% | |
| `c14_11_1_1` | object | 78.7% | |
| `c14_1b_1_1` | object | 69.4% | |
| `c14_4b_1_1` | object | 68.9% | |
| `c14_8b_1_1` | object | 74.6% | |
| `c15_1_1` | object | 21.1% | |
| `v22` | object | 67.0% | |
| `c16_1_1_2` | object | 67.0% | |
| `c16_1_2_2` | object | 67.0% | |
| `c16_1_3_2` | object | 67.0% | |
| `c16_1_4_2` | object | 67.0% | |
| `c16_1_5_2` | object | 67.0% | |
| `c16_1_6_2` | object | 67.0% | |
| `c16_1_7_2` | object | 67.0% | |
| `c16_1_8_2` | object | 67.0% | |
| `c16_1_9_2` | object | 67.0% | |
| `c16_1_10_2` | object | 67.0% | |
| `c16_1_11_2` | object | 67.0% | |
| `c13_1b_2` | object | 67.0% | |
| `c13_1b_1_2` | object | 67.0% | |
| `c13_1b_2_2` | object | 67.0% | |
| `c13_1b_3_2` | object | 67.0% | |
| `c13_1b_4_2` | object | 67.0% | |
| `c13_1b_5_2` | object | 67.0% | |
| `c13_1b_6_2` | object | 67.0% | |
| `c15_1_2` | object | 67.0% | |
| `psysym_1` | object | 23.2% | |
| `c17_1` | object | 23.2% | |
| `c18_1` | object | 49.0% | |
| `c18_1_1` | object | 49.0% | |
| `c18_2_1` | object | 49.0% | |
| `c18_3_1` | object | 49.0% | |
| `c18_4_1` | object | 49.0% | |
| `c18_5_1` | object | 49.0% | |
| `c18_6_1` | object | 49.0% | |
| `c18_7_1` | object | 49.0% | |
| `c18_8_1` | object | 49.0% | |
| `c18_9_1` | object | 49.0% | |
| `c18_10_1` | object | 49.0% | |
| `c18_11_1` | object | 49.0% | |
| `c18_12_1` | object | 49.0% | |
| `c18_13_1` | object | 49.0% | |
| `c18_14_1` | object | 49.0% | |
| `c18_15_1` | object | 49.0% | |
| `c18_16_1` | object | 49.0% | |
| `c18_17_1` | object | 49.0% | |
| `c18_18_1` | object | 49.0% | |
| `c18_19_1` | object | 49.0% | |
| `c18_20_1` | object | 49.0% | |
| `c18_21_1` | object | 49.0% | |
| `psysym_2` | object | 61.3% | |
| `c17_2` | object | 61.3% | |
| `c18_2` | object | 74.7% | |
| `c18_1_2` | object | 74.7% | |
| `c18_2_2` | object | 74.7% | |
| `c18_3_2` | object | 74.7% | |
| `c18_4_2` | object | 74.7% | |
| `c18_5_2` | object | 74.7% | |
| `c18_6_2` | object | 74.7% | |
| `c18_7_2` | object | 74.7% | |
| `c18_8_2` | object | 74.7% | |
| `c18_9_2` | object | 74.7% | |
| `c18_10_2` | object | 74.7% | |
| `c18_11_2` | object | 74.7% | |
| `c18_12_2` | object | 74.7% | |
| `c18_13_2` | object | 74.7% | |
| `c18_14_2` | object | 74.7% | |
| `c18_15_2` | object | 74.7% | |
| `c18_16_2` | object | 74.7% | |
| `c21` | object | 0.9% | |
| `c22` | object | 0.0% | |
| `c23` | object | 78.9% | |
| `c24` | object | 71.3% | |
| `c25` | object | 71.3% | |
| `c26` | object | 28.7% | |
| `d1_1` | object | 0.0% | |
| `d1_2` | object | 0.0% | |
| `d1_3` | object | 0.0% | |
| `d1_4` | object | 0.0% | |
| `d1_5` | object | 0.0% | |
| `d1_6` | object | 0.0% | |
| `d1_7` | object | 0.0% | |
| `d1_8` | object | 0.0% | |
| `d1_9` | object | 0.0% | |
| `d2` | object | 0.0% | |
| `d3` | object | 0.0% | |
| `end_time` | datetime64[ns] | 0.0% | |
| `live_with_family` | object | 0.0% | |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |
---
## Numeric Summary
| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `c1_1a` | 2012.0 | 2021.0 | 2020.8389 | 2021.0 |
| `c1_1b` | 2010.0 | 2021.0 | 2020.8337 | 2021.0 |
| `c1_1c` | 2000.0 | 2021.0 | 2020.6079 | 2021.0 |
---
## 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`. 1240 column(s) with >80% missing values were removed: `c1_3a`, `c1_3c`, `c1_1d`, `c1_2d`, `c1_3d`, `c1_4d`.... The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.
---
## Limitations
- Data originates from Wellcome (inactive) 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: `education`, `marital_stat`, `c1_1a`, `c1_2a`, `c1_4a`, `c1_1b`, `c1_2b`, `c1_3b`....
- This dataset spans 2 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/long-covidresearchagenda) for the publisher's own methodology notes and caveats.
---
## Citation
```bibtex
@dataset{hdx_africa_long_covidresearchagenda,
title = {Kenya, Malawi, Long Covid-19 effects survey dataset},
author = {Wellcome (inactive)},
year = {2024},
url = {https://data.humdata.org/dataset/long-covidresearchagenda},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
```
---
*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*
annotations_creators:
- 无注释
language_creators:
- 获取式
language:
- 英语
license: CC BY-SA 4.0
multilinguality:
- 单语言
size_categories:
- 样本量小于1000
source_datasets:
- 原始数据集
task_categories:
- 表格分类
task_ids: []
tags:
- 非洲
- 人道主义
- HDX（Humanitarian Data Exchange）
- electric-sheep-africa
- COVID-19
- 健康
- 医疗设施
- 后勤
- 肯尼亚（KEN）
- 马拉维（MWI）
pretty_name: "肯尼亚、马拉维长新冠（Long COVID-19）影响调查数据集"
---
# 肯尼亚、马拉维长新冠影响调查数据集
**发布方：** 惠康基金会（已停止运营） · **来源：** [HDX（Humanitarian Data Exchange）](https://data.humdata.org/dataset/long-covidresearchagenda) · **许可证：** `cc-by-sa` · **更新时间：** 2024-05-10
---
## 摘要
本研究在肯尼亚与马拉维开展，旨在深入了解长新冠（Long COVID-19）对感染者、照护者以及接诊医护人员的影响，最终形成一份研究议程，列出了指导未来研究方向的优先级事项。
本数据集的每一行均代表国家级汇总数据。时间覆盖范围由`start_time`、`end_time`列标注。地理覆盖范围：**KEN（肯尼亚）、MWI（马拉维）**。
*本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为可供机器学习使用的Parquet（帕克尔）格式。*
---
## 数据集特征
| 类别 | 详情 |
|---|---|
| **研究领域** | 公共卫生 |
| **观测单元** | 国家级汇总数据 |
| **总数据行数** | 806 |
| **列数** | 668（其中3列为数值型，663列为分类变量，2列为日期时间型） |
| **训练集划分** | 644行 |
| **测试集划分** | 161行 |
| **地理覆盖范围** | KEN、MWI |
| **发布方** | 惠康基金会（已停止运营） |
| **HDX最后更新时间** | 2024-05-10 |
---
## 变量
**地理类变量** — `ea_type`（城市、农村）、`body_organ_disease`、`respiratory_condition`、`country`、`symptoms`及另外41个变量。
**时间类变量** — `start_time`、`end_time`。
**人口统计学类变量** — `gender`（男性、女性）、`age`（18-33岁、34-50岁、>50岁）。
**标识符/元数据类变量** — `instanceid`（通用唯一识别码UUID：a3dc17ef-9135-4ff4-ad5e-2451fda3b672、UUID：d6bdb6ec-8428-4f31-b221-a36b685abe52、UUID：fb4f0935-193d-4a13-84dc-dd5bfd8843aa）、`esa_source`、`esa_processed`。
**其他类变量** — `education`（中等教育、高等教育、初等教育）、`religion`（基督教、其他宗教、无宗教信仰）、`marital_stat`（已婚、无伴侣、同居）、`occupation`（在职、失业）、`no_occupants`（1-3人、4人及以上、0人）及另外610个变量。
---
## 快速入门
python
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-long-covidresearchagenda")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()
print(train.shape)
train.head()
---
## 数据结构
| 列名 | 数据类型 | 缺失率 | 取值范围/示例值 |
|---|---|---|---|
| `instanceid` | 字符型 | 0.0% | UUID：a3dc17ef-9135-4ff4-ad5e-2451fda3b672、UUID：d6bdb6ec-8428-4f31-b221-a36b685abe52、UUID：fb4f0935-193d-4a13-84dc-dd5bfd8843aa |
| `ea_type` | 字符型 | 0.0% | 城市、农村 |
| `start_time` | datetime64[ns] | 0.0% | |
| `gender` | 字符型 | 0.6% | 男性、女性 |
| `age` | 字符型 | 2.5% | 18-33岁、34-50岁、>50岁 |
| `education` | 字符型 | 58.4% | 中等教育、高等教育、初等教育 |
| `religion` | 字符型 | 5.0% | 基督教、其他宗教、无宗教信仰 |
| `marital_stat` | 字符型 | 30.1% | 已婚、无伴侣、同居 |
| `occupation` | 字符型 | 8.3% | 在职、失业 |
| `no_occupants` | 字符型 | 18.5% | 1-3人、4人及以上、0人 |
| `communicable_disease` | 字符型 | 2.4% | 否 |
| `non_communicable_disease` | 字符型 | 0.5% | |
| `body_organ_disease` | 字符型 | 0.9% | |
| `neurological_condition` | 字符型 | 0.1% | |
| `respiratory_condition` | 字符型 | 2.6% | |
| `other_disease` | 字符型 | 1.1% | |
| `country` | 字符型 | 0.0% | |
| `symptoms` | 字符型 | 0.1% | |
| （后续列因篇幅过长省略，保留原表格式） | | | |
---
## 数值型变量统计摘要
| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `c1_1a` | 2012.0 | 2021.0 | 2020.8389 | 2021.0 |
| `c1_1b` | 2010.0 | 2021.0 | 2020.8337 | 2021.0 |
| `c1_1c` | 2000.0 | 2021.0 | 2020.6079 | 2021.0 |
---
## 数据整理流程
原始数据通过CKAN API从HDX下载，并转换为Parquet格式。列名均转换为小写并标准化为蛇形命名法（snake_case）。将常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为`NaN`。移除了1240列缺失值占比超过80%的字段（如`c1_3a`、`c1_3c`、`c1_1d`、`c1_2d`、`c1_3d`、`c1_4d`……）。本数据集以固定随机种子（42）按照80/20的比例划分为训练集与测试集，并以Snappy压缩的Parquet格式存储。
---
## 局限性说明
- 数据源自已停止运营的惠康基金会，未由Electric Sheep Africa进行独立验证。
- 自动化清洗无法修正原始调研中存在的错报值、定义不一致或抽样偏差问题。
- 以下列的缺失值占比超过20%，在建模过程中需谨慎使用：`education`、`marital_stat`、`c1_1a`、`c1_2a`、`c1_4a`、`c1_1b`、`c1_2b`、`c1_3b`……
- 本数据集覆盖2个国家，各国间的地理与方法学差异可能影响跨国家比较的有效性。
- 请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/long-covidresearchagenda)获取发布方提供的方法学说明与注意事项。
---
## 引用格式
bibtex
@dataset{hdx_africa_long_covidresearchagenda,
title = {肯尼亚、马拉维长新冠影响调查数据集},
author = {惠康基金会（已停止运营)},
year = {2024},
url = {https://data.humdata.org/dataset/long-covidresearchagenda},
note = {由Electric Sheep Africa重新打包以适配机器学习需求（https://huggingface.co/electricsheepafrica)}
}
---
*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — 非洲机器学习数据集基础设施。尼日利亚拉各斯。*
提供机构：
electricsheepafrica
