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メキシコのクラスターベースのデング熱予測のためのトポロジカルデータ分析
1Departamento de Matemáticas, Facultad de Ciencias, Universidad Nacional Autónoma de México, Circuito Exterior s/n, Ciudad Universitaria, 04510, Ciudad de México, Mexico.
Spatial and spatio-temporal epidemiology
|February 19, 2026
まとめ
トポロジカルデータ分析 (TDA) は,形状認識機能をクラスタ化された一般化された線形モデル (GLM) に統合することにより,メキシコにおけるデング熱発生率の予測を強化します. 短期的なスキルを向上させないものの,TDAは,地域の監視のための解釈性とパターンの分層化を改善します.
科学分野:
- エピデミオロジー エピデミオロジー
- コンピュータ生物学 コンピュータ生物学
- データサイエンス データサイエンス
背景:
- デング熱の発生率は,伝統的な予測に挑戦する複雑でダイナミックなパターンを提示します.
- トポロジカルデータ分析 (TDA) は,複雑なデータ構造の形状認識ディスクリプタを提供します.
研究 の 目的:
- メキシコにおける毎週デング熱の発生率に関するクラスター化予測パイプラインの開発と評価.
- TDAを機能エンジニアリングに統合し,より優れた流行病パターン分析と予測を行う.
主な方法:
- TDA (persistence diagrams, persistence images) とガウス混合クラスタリングとクラスタレベルの一般化された線形モデル (GLM) を組み合わせたパイプラインです.
- ベースラインに対するローリング起源評価を用いたポアソンとネガティブ・バイノミアル (NB2) モデルの比較.
- TDA/PIが予測スキル,解釈可能性,パターン層分化に与える影響の評価.
主要な成果:
- ネガティブ・バイノミアル (NB2) モデルは3つのクラスターでポアソンモデルを一貫して上回り,より低い平均絶対誤差 (MAE) を示しました.
- TDAと持続画像 (PI) を含めると,1週間の予測の精度には大きな変化はなかったが,解釈性と層格化は大幅に改善された.
- PIの分析は,デング熱の発生率における独特でクラスタ固有の動態パターンを強調した.
結論:
- 開発されたワークフローは,再現可能な,運用可能な,クラスターベースのデング熱の予測を提供します.
- TDAは,疫病動態の理解を向上させ,地域的な監視努力を支援します.
- TDAと組み合わせたNB2モデルは,デング熱の予測に堅実なアプローチを提供します.
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