相关实验视频
Updated: Jan 7, 2026

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A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
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使用分数SIRD和深度学习预测流行病动态.
Ramsha Shafqat1, Kinda Abuasbeh2, Salma Trabelsi3
1Department of Mathematics and Statistics, The University of Lahore, Sargodha, 40100, Pakistan. ramshawarriach@gmail.com.
Scientific reports
|December 31, 2025
概括
这项研究引入了一个分数SIRD模型,其中包括记忆效应和疾病引起的死亡率. 整合微积分和深度学习可以提高流行病预测的准确性.
科学领域:
- 流行病学 流行病学
- 数学生物学 数学生物学
- 计算科学 计算科学
背景情况:
- 传统的SIRD模型往往缺乏记忆效应和详细的死亡率跟踪.
- 分数计算提供了一个框架,将记忆纳入流行病动态.
- 准确的流行病预测对于公共卫生干预至关重要.
研究的目的:
- 引入和分析使用规范化的卡普托-法布里齐奥衍生物的分数顺序SIRD流行病模型.
- 将记忆效应和疾病引起的死亡率纳入SIRD框架.
- 开发和验证数值方案,并整合深度学习以提高预测.
主要方法:
- 开发一个分数顺序的SIRD模型与一个规范化的卡普托-法布里齐奥导数.
- 建立存在,独特性,积极性和人口保护性质.
- 实施一个强大的数值方案和深度神经网络 (DNN) 进行近似.
- 模拟分析以证明内存参数和内核正常化的影响.
主要成果:
- 与NCF衍生物的分数SIRD模型成功地结合了记忆效应和死亡率.
- 数值方案被证明是稳健的,DNN准确地近似了分数SIRD动态.
- 模拟突出了记忆参数对于流行病预测的重要性.
- 综合模型实现了高预测准确度,平均平方误差低 (0.00027) 和根平均平方误差低 (<0.17).
结论:
- 拟议的分数SIRD模型为了解流行病动态提供了更全面的框架.
- 分数微积分和深度学习的整合为准确的流行病预测提供了一个强大的工具.
- 这种方法为公共卫生决策和疾病控制策略提供了宝贵的见解.
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