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感染病の予測を評価し,スコアを割り当てるルール
Aaron Gerding1, Nicholas G Reich1, Benjamin Rogers1
1Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts at Amherst, Amherst, Massachusetts, USA.
まとめ
新しい予測評価指標の開発は 感染症政策の最適化に不可欠です この研究は,従来の精度測定を上回る,満たされていない医療ニーズを最小限に抑えるための政策の成功をよりよく反映する,配分スコアリングのルールを導入しています.
科学分野:
- 流行病学について
- 公衆衛生
- 健康 経済
背景:
- 感染症の予測は公衆衛生政策にとって不可欠です.
- 既存の予測評価指標は,資源配分などの政策目標と一致しない可能性があります.
- 予測の正確さを現実の政策結果と結びつける研究は限られている.
研究 の 目的:
- 感染症の予測と政策決定の関連性を探求する.
- 資源配分に基づく新しい予測スコアリングルを開発し評価する.
- この新しい指標が,従来の指標よりも,政策上の有用性を把握しているかどうかを評価する.
主な方法:
- 地域疾病負担 (例えば,COVID-19入院) の確率予測を用いた.
- 限られた医療資源の配分を最適化し,満たされていないニーズを最小限に抑えるための配分スコアリングルを開発しました.
- 配分スコアルールによる予測ランキングと,加重区間スコアを比較した.
主要な成果:
- 配分スコア規則は,重み付けのインターバルスコアと比較して異なる予測スキルランキングを生み出しました.
- これは,従来の精度メトリクスが見逃した予測値をアロケーションルールで捉えていることを示唆しています.
- 資源配分に最適化された予測は,政策に係るパフォーマンスの改善を示した.
結論:
- 伝統的な予測の精度指標は,政策のための予測の価値を完全に反映していないかもしれません.
- 政策の成果に直接結びついている 配分スコアリング規則は 疫病予測の評価のための有望なアプローチです
- 政策目標に関連したスコア付けのルールを設計することで,感染症の予測の有用性を高めることができます.
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