计算框架和机器学习方法对分数顺序的土壤虫感染控制机制的疾病模型
Kottakkaran Sooppy Nisar1, Muhammad Farman2,3, Muhammad Waseem4
1Department of Mathematics, College of Science and Humanities, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia. n.sooppy@psau.edu.sa.
这项研究使用分数计算和机器学习来模拟土壤传播的虫感染 (STH). 这些发现表明,STH感染可以治愈,为改善公共卫生策略铺平了道路.
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:
- 在全球范围内,土壤传播的虫感染影响了15亿人.
- 准确的建模对于有效的控制策略至关重要.
研究的目的:
- 模拟STH传输动态使用分数式微积分和机器学习.
- 提高预测准确度,并为STH感染的控制策略提供信息.
主要方法:
- 开发了STH传输的分数顺序微积分模型.
- 使用的机器学习技术:ANNLMB,随机森林和SVM.
- 分析了模型稳定性,平衡点,并进行了灵敏度分析.
主要成果:
- 当基本繁殖数小于1时,证明了无疾病平衡的局部和全球非对称稳定性.
- 确定了传染率,招募率和寄生虫携带能力作为关键敏感参数.
- 与ANNLMB.实现了高预测准确度 (RF为99.83%,SVM为99.5%) 和低平均平方误差.
结论:
- 分数计算和机器学习集成为STH建模提供了强大的方法.
- 该模型表明,STH感染可能是可以治愈的.
- 机器学习增强的模型可以支持实时监测和针对性干预,以改善公共卫生.
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