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微分COVID-19模型的计算建模 哈尔波波合方法与真实数据的方法
Rahat Zarin1, Usa Wannasingha Humphries1, Amir Khan2
1Department of Mathematics, Faculty of Science, King Mongkut's University of Technology, Thonburi (KMUTT), 126 Pracha-Uthit Road, Bang Mod, Thrung Khru, Bangkok 10140, Thailand.
这项研究模型使用分数COVID-19模型和Haar波段来模拟Omicron变异的传播. 数字模拟为针对SARS-CoV-2的公共卫生策略提供了见解.
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:
- SARS-CoV-2 的Omicron 变种呈现出独特的传播动态.
- 分数阶微分方程为模拟疾病传播提供了更细致的方法.
- 准确的建模对于有效的公共卫生干预至关重要.
研究的目的:
- 开发和分析一个包含Omicron变异特征的小数顺序COVID-19模型.
- 为了准确的数值解决方案,采用哈尔波纹调配方法.
- 为减轻Omicron变种影响提供数据驱动的洞察力.
主要方法:
- 使用卡普托意义上的分数导数用于COVID-19流行病模型.
- 通过固定点理论确定模型解决方案的存在和独特性.
- 在数值模拟和参数估计中应用哈尔波小组分类方法.
- 进行灵敏度分析以确定模型的关键参数.
主要成果:
- 该研究成功模拟了Omicron变种的传播动态.
- 哈尔波小组聚合方法在分数模型中被证明是高效和准确的.
- 敏感性分析确定了影响病毒传播的关键参数.
- 参数估计使用来自印度的真实世界COVID-19数据进行.
结论:
- 分数顺序模型为SARS-CoV-2变种传播提供了宝贵的见解.
- 数字模拟有助于理解流行病的动态,并为公共卫生政策提供信息.
- 哈尔波纹法是分析复杂流行病学模型的强大工具.
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