使用深度强化学习方法模拟和控制COVID-19疾病
Nazanin Ghazizadeh1, Sajjad Taghvaei1, Seyyed Arash Haghpanah2
1School of Mechanical Engineering, Shiraz Univeristy, P.O.B. 7134851154, Shiraz, Iran.
Medical & biological engineering & computing
|June 28, 2024
概括
研究人员开发了一种新的SQEIAR模型,使用最佳控制策略控制COVID-19的传播. 该模型显著减少了死亡和症状感染,证明了对干扰的强度.
科学领域:
- 流行病学和公共卫生.
- 计算机建模和控制理论
背景情况:
- COVID-19 疫情对全球健康和经济造成了毁灭性的影响,需要有效的疾病控制策略.
- 现有的流行病学模型往往缺乏细节性,无法充分捕捉疾病传播和干预有效性的复杂动态.
研究的目的:
- 引入和评估COVID-19的新型SQEIAR流行病学模型.
- 应用最佳的控制方法,以尽量减少疾病的流行率和干预成本.
- 评估在各种不确定性下,拟议的控制策略的稳定性.
主要方法:
- 开发了SQEIAR (易受感染,隔离,暴露,传染-症状,传染-无症状,恢复) 模型,包括六个人口群体.
- 整合了三个控制输入:敏感个体的隔离,疫苗接种和治疗.
- 采用深度决定性政策梯度 (DDPG) 算法进行最佳控制,以最大限度地减少症状个体和成本.
- 在不同的控制场景下模拟模型并评估结果,包括在噪声和参数不确定性下进行稳定性测试.
主要成果:
- 相比于不受控制的模型,最佳控制策略减少了60%的死亡率和74%的症状感染率.
- DDPG算法成功地确定了各种干预场景的最佳控制输入.
- 控制系统在受观察者变量,控制输入和模型参数噪声影响时表现出显著的稳定性.
结论:
- 基于DDPG的最佳控制的SQEIAR模型为管理COVID-19等传染病爆发提供了一个强大的框架.
- 建议的干预策略有效地减轻了疾病传播和死亡率.
- 控制系统的稳定性确保可靠的性能,即使在现实世界的不确定性存在.
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