强化学习可以缓解疫苗接种困境
Yikang Lu1, Yanan Wang2, Yifan Liu3
1School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, Yunnan 650221, China.
Chaos (Woodbury, N.Y.)
|July 6, 2023
概括
这项研究使用人工智能 (AI) 强化学习,特别是布什-莫斯泰勒 (BM) 模型,来改进疫苗接种策略,克服疫苗接种困境. 决策规则的更高敏感性导致疫苗接种覆盖率增加和成本过渡的延迟.
科学领域:
- 流行病学 流行病学
- 人工智能的人工智能
- 网络科学 网络科学
背景情况:
- 流行病的传播是一个重大的公共卫生问题.
- 决策规则影响疫苗接种行为和覆盖范围.
- 复杂的网络为疾病传播建模带来了挑战.
研究的目的:
- 调查基于人工智能 (AI) 的布什-莫斯泰勒 (BM) 模型的决策规则如何影响流行病在复杂网络上传播.
- 分析两个独立规则 (固定损失和平均回报) 对疫苗接种行为的影响.
- 确定人工智能驱动的学习是否可以帮助克服疫苗接种困境.
主要方法:
- 利用了布什-莫斯泰勒 (BM) 模型,一种强化学习方法,结合了疫苗接种和流行病学过程.
- 基于固定损失考虑和邻居的平均回报的模拟剂疫苗接种行为更新.
- 模拟场景与不同的刺激,包括损失的回报和环境变化.
主要成果:
- 决策规则的更高敏感性导致疫苗接种覆盖率的增加.
- 增加的敏感性延迟了从完全到不完全接种疫苗的相对疫苗接种成本的过渡点.
- 疫苗接种困境在一定程度上被克服了,疫苗接种概率显示正常或倾斜的分布.
结论:
- 与人工智能集成的布什-莫斯泰勒 (BM) 模型提供了一种有希望的方法来增强疫苗接种策略.
- 优化决策规则可以显著提高疫苗接种覆盖率,减轻流行病的传播.
- 人工智能授权的学习有可能解决公共卫生中持续存在的疫苗接种困境.
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