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Updated: Jun 7, 2025

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使用数学建模和深度学习进行传染病控制的综合框架
Mohammed Salman1, Pradeep Kumar Das2, Sanjay Kumar Mohanty1
1School of Advanced SciencesVellore Institute of Technology Vellore 632014 India.
IEEE open journal of engineering in medicine and biology
|November 20, 2024
概括
这项研究引入了一种新的传染病预测系统,结合了决定性,随机和深度学习模型. 综合方法提高了预测的准确性,并分析了疫苗接种和时间延迟对疾病传播的影响.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 传染病对全球健康构成重大挑战.
- 准确的建模对于有效的疾病控制策略至关重要.
- 数据不平衡,噪音和强度不均性使疾病检测复杂化.
研究的目的:
- 提出一种新的传染病模式预测系统.
- 整合决定性,随机和深度学习模型,以改善预测.
- 调查时间延迟对感染和疫苗接种率的影响.
主要方法:
- 在无疾病平衡状态下的全球稳定性的分析,使用Routh-Haurwitz标准和Lyapunov方法.
- 使用非线性沃尔特拉积分方程分析特有平衡.
- 应用深度学习模型来预测疫苗接种对感染率的影响,利用长期依赖.
主要成果:
- 综合模型显示了更好的预测性能.
- 该研究量化了时间延迟对感染和疫苗接种动态的影响.
- 深度学习组件准确地预测了疫苗接种对感染率的影响.
结论:
- 拟议的框架为传染病建模和预测提供了一个强大的方法.
- 该模型有效地分析了考虑到疫苗接种和移民率的稳定性.
- 深度学习的整合提高了疾病控制干预措施的预测准确性.
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