新興感染症のモデル化における機会と課題

C Jessica E Metcalf^1,2, Justin Lessler³

¹Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA. cmetcalf@princeton.edu.

Science (New York, N.Y.)

|July 15, 2017

まとめ

数学的モデルによって感染症の感染拡大と制御を予測できますデータが限られているにも関わらずデータと方法の革新は新興病原体に対する公衆衛生対策の力を高めています

科学分野:

流行病学
数学生物学
公衆衛生

背景:

病原体の出現には,新種のウイルス,新しい集団への感染,薬剤耐性が含まれます.
数学的モデルは病気の発生を予測し感染伝播を理解し制御戦略を評価するのに不可欠です
データ不足は,これらのモデルの効果的な使用に重大な制約をもたらす.

研究の目的:

病原体の出現の文脈における数学的モデルの能力と限界を強調する.
データと方法論の進歩により,これらのモデルの成長の可能性を強調する.
モデルを公衆衛生の実践と統合して対応を改善することを提唱する.

主な方法:

新興病原体の数学モデリングの現状と限界のレビュー.
データの可用性 (遺伝学,生態学) とコンピューティングの革新がモデルの有用性を向上させる方法についての議論.
感染症モデルを公衆衛生の実践に組み込む必要性を探求する.

主要な成果:

数学的モデルは,新興病原体の制御を予測し評価するための貴重なツールを提供します.
データ不足は依然として重要な課題ですが,新しいデータソースと方法によって解決されています.
イノベーションにより公共衛生への対応に役立つモデルの力を高めています

結論:

感染症のモデルを公衆衛生の実践とより密接に統合することが不可欠です.
モデルの適用のための容易に利用可能なリソースの開発は,応答のタイミングと質を向上させることができます.
強化されたモデリング機能は,将来の病原体発生イベントの管理に有望である.

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