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SEINN:对于随机流行病模型的深度学习算法
Thomas Torku1, Abdul Khaliq2, Fathalla Rihan3
1University Studies Department, Middle Tennessee State University, Murfreesboro, TN 37132, USA.
Mathematical biosciences and engineering : MBE
|November 3, 2023
概括
随机模型为疾病传播提供了比确定性模型更好的预测. 减少随机性和增加疫苗接种率改善了非线性发病率预测,有助于政策制定.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 随机模型比决定性模型提供了更全面的系统洞察力,特别是错误指标.
- 了解随机性对流行病动态的影响对于有效的公共卫生战略至关重要.
研究的目的:
- 开发一个新的随机流行病学信息的神经网络 (SEINN).
- 研究疫苗接种和随机性对流行病模型中非线性发病率的联合影响.
主要方法:
- 开发了一个深度神经网络 (SEINN) 来学习随机流行病模型的参数和动态.
- 分析了考虑到疫苗接种和随机性的非线性发病率.
- 进行计算分析,包括灵敏度和过拟合分析.
主要成果:
- 随机性减少和疫苗接种率增加与改善的非线性发病率预测相关.
- 随机模型提供了对疾病传播复杂性的更细致的理解.
- 提出的SEINN方法在计算分析方面表现出了效率.
结论:
- SEINN提供了一种有效的方法来建模随机流行病的动态.
- 调查结果指导决策者将随机性和疫苗接种纳入流行病控制战略.
- 使用田纳西州数据的案例研究验证了该模型对现实世界场景的适用性.
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