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Dataset
electricsheepafrica

electricsheepafrica/africa-synth-malaria-malaria-burden-spatiotemporal-all

Africa Synth Malaria Malaria Burden Spatiotemporal All | Africa (Electric Sheep Africa metadata inventory) Size category: 10K<n<100K - Formats: csv - Sector: health - Engineered by Electric Sheep Africa TL;DR This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context. What This Dataset Covers… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-malaria-malaria-burden-spatiotemporal-all.

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TINIX TRENDING·Chủ đề: malaria

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TINIX TRENDING·Chủ đề: electric-sheep-africa

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TINIX TRENDING·Chủ đề: malaria-burden

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🧬 Dataset giả lập gánh thành bệnh sốt rét ở Nigeria qua không gian và thời gian

Dataset giả lập được sinh từ tham số hóa trên cơ sở tài liệu SSA, không phải quan sát thực, phù hợp cho huấn luyện mô hình chứ không phân tích chính sách.

Đây là một dataset thuộc danh mục Electric Sheep Africa trên Hugging Face, nhằm hỗ trợ khám phá dữ liệu châu Phi thông qua metadata chuẩn hoá, hướng dẫn tải và bối cảnh phân tích. Dataset mô phỏng gánh thành bệnh sốt rét ở Nigeria từ 2018–2025, tích hợp các biến khí hậu, hiệu ứng can thiệp và chỉ số chất lượng báo cáo.

✨ Điểm nổi bật

  • Synthetic data: Sinh ra từ tham số hóa dựa trên tài liệu SSA, không phải quan sát thực tế.
  • Phạm vi địa lý: Tập trung vào Nigeria (ISO3: NGA), phục vụ nghiên cứu khu vực SSA.
  • Kênh thời gian: Dữ liệu từ 2018–2025, hỗ trợ phân tích xu hưởng dài hạn.
  • Biến số đa dạng: Bao gồm biến khí hậu, can thiệp y tế và chỉ số chất lượng báo cáo.
  • Định dạng CSV: Dễ tích hợp với công cụ phân tích tabular như Pandas, Dask, Polars.
  • License mở: Chia sẻ dưới giấy phép CC BY 4.0, hỗ trợ tái sử dụng và trích dẫn.
  • Hướng dẫn sử dụng: Cung cấp ví dụ tải bằng datasets và chuyển đổi sang Pandas.

💡 Vì sao đáng chú ý

Không phải là dữ liệu thực, nhưng dataset này cung cấp một môi trường kiểm thử ổn định cho các mô hình dự báo gánh thành bệnh sốt rét, đặc biệt hữu ích khi dữ liệu thực khó tiếp cận. Kết hợp các biến khí hậu và can thiệp, nó cho phép kiểm tra giả thuyết và phát triển pipeline ML một cách nhanh chóng.


#Malaria #SyntheticData #Nigeria #Healthcare #TiniX

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