感染症の伝染の社会的側面について明確に考える
Caroline Buckee1, Abdisalan Noor2, Lisa Sattenspiel3
1Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA. cbuckee@hsph.harvard.edu.
Nature
|July 1, 2021
まとめ
社会的・文化的要因は 感染症の蔓延と疫病への対応に大きな影響を及ぼします 地元の知識と新しいデータ源を統合することで 公衆衛生モデルや 病気の伝染における人間の行動の理解を 改善できます
科学分野:
- エピデミオロジー
- 社会科学
- 公衆衛生
背景:
- 感染症の伝播は,社会的,文化的ダイナミクスによって深く影響され,接触パターン,曝露,健康を求める行動,介入の受け入れに影響されます.
- 疫学におけるこれらの社会的側面を量化することは,歴史的に政策立案のためのモデリングフレームワークの一般化に挑戦してきました.
- モバイル端末や技術から得られた新しいデータは 病気の伝染の行動要因を 研究する新たな機会を提供します
研究 の 目的:
- 感染症のダイナミクスや 疫病への対応を 社会的・文化的要因がどう形づくるかを 探求する.
- 疫学モデルに新しいデータストリームと地元の知識を統合する可能性を議論する.
- 感染症の状況における人間の行動に関する政策関連モデルと理論の開発を進める.
主な方法:
- 感染病の伝染に対する社会的・文化的要因の影響の検討と議論
- 行動ドライバーのプロキシとして,モバイルデバイスデータなどの新しいデータソースの探索.
- モデル開発と解釈のための定量データと地域の知識の統合を重視する.
主要な成果:
- 社会構造 (親族,家庭,社会) は,直接感染に不可欠な接触パターンを決定します.
- 社会経済的,文化的要因は 暴露,健康上の結果,診断,報告,介入の遵守に影響します.
- 新しい技術は 病気のダイナミクスに 関連する人間の行動の 価値あるものですが 不完全なものを提供します
結論:
- 地元の知識をモデル設計とデータ解釈に組み込むことは,政策に係る公衆衛生のツールの作成に不可欠です.
- このアプローチは,感染症に関する人間の行動を説明するより堅牢で一般化可能な理論につながります.
- 社会科学の洞察と疫学モデルの間のギャップを埋めることは 疫病の効果的な管理に不可欠です
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