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波森随机测量流行病学先验中的噪声引起的连贯性,深度神经网络得到了信息,以确定病毒动态的强度
Saima Rashid1, Ayesha Siddiqa1, Fekadu Tesgera Agama2
1Department of Mathematics, Government College University, Faisalabad, 38000, Pakistan.
Scientific reports
|May 17, 2025
概括
本研究引入了一种混合模型,将流行病学分隔系统和人工神经网络结合起来,以分析 (MPV) 传播动态. 这种新的方法增强了对MPV传播的理解和预测,以改善公共卫生战略.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 麻疹 (MPV) 传播分析面临着传统的隔间模型和纯机器学习方法的挑战.
- 现有的方法与参数复杂性和MPV进展中缺乏可解释性作斗争.
研究的目的:
- 开发一种混合模型,集成基于微分方程的分区系统和人工神经网络 (ANN) 来分析MPV传输.
- 提高对MPV复杂传输机制的理解,提高预测准确度.
主要方法:
- 开发了一种用于人类的随机SEIQR模型和用于动物的SEI模型,将Poisson随机测量噪声纳入其中.
- 莱文伯格·马奎特反向传播神经网络 (LMBNNS) 用于培训,将分区框架与ANN结合起来.
- 确定了一个值参数,以确定驱动MPV感染的因素,并建立了消除它的标准.
主要成果:
- 该研究证明了模型的全球积极解决方案的存在和独特性.
- 确定了一个值参数,表示MPV消除的条件 ([公式:见文本]) 或持久性 ([公式:见文本]).
- 混合模型表现出高准确度,通过最小的绝对误差和有效的预测能力来验证.
结论:
- 混合方法为分析传染病动态提供了一个强大的工具,捕捉固有的不确定性.
- 这种方法提高了流行病的预测和预防,导致对MPV的更有效的公共卫生干预措施.
- 随机过程和机器学习的整合为实时疾病监测提供了更准确的框架.
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