传染病的动力学数学模型通过无监督的随机神经网络范式
Waseem1, Sabir Ali2, Aatif Ali3
1School of Mechanical Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China.
Computational biology and chemistry
|December 4, 2024
概括
数学建模和一种新的随机神经网络被用来分析冠状病毒大流行. 这种方法有效地模拟了疾病传播和疫苗接种的影响,为控制策略提供了准确的预测.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 数学建模的数学建模
背景情况:
- 全球病毒爆发对社会和经济产生重大影响,造成广泛的痛苦和死亡.
- 数学建模对于理解和减轻冠状病毒爆发等流行病的严重程度至关重要.
- 以前的模型已经提供了洞察力,但需要先进的计算方法来提高准确性.
研究的目的:
- 提出和评估一个决定性的六分区数学模型,与一种新的随机神经网络集成,用于流行病分析.
- 通过数值模拟和灵敏度分析来评估模型的意义.
- 调查疫苗在减少社会压力和防止疾病在整个社区传播方面的作用.
主要方法:
- 开发一个决定性的六部分数学模型.
- 整合一个新的随机神经网络用于预测分析.
- 数字模拟,灵敏度分析,图形表示和表格数据呈现.
- 对拟议模型的准确性和稳定性的比较,与已建立的数值解法器进行比较.
主要成果:
- 数字模拟证明了拟议模型的意义,与灵敏度分析结果保持一致.
- 随机神经网络的有效性通过数值模拟得到验证.
- 调查证实了疫苗接种对减少社会压力的积极影响.
- 模拟提供了关于预防社区范围内的疾病传播的见解.
- 拟议的随机神经网络与其他数值解法器相比,实现了最小的残余误差.
结论:
- 综合数学和随机神经网络模型为分析和控制病毒流行病提供了强大而准确的方法.
- 随机神经网络在流行病学建模中显示出显著的希望,为疾病传播和干预策略提供可靠的预测.
- 疫苗接种仍然是减轻社会影响和防止传染病广泛传播的关键工具.
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