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Updated: Jan 31, 2026

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
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增强数据和神经网络用于可靠的流行病预测:在意大利应用于COVID-19

Giacomo Dimarco1, Federica Ferrarese1, Lorenzo Pareschi1,2

  • 1Department of Mathematics and Computer Science & Center for Modeling, Computing and Statistics (CMCS), University of Ferrara, via Machiavelli 30, 44121 Ferrara, Italy.

Mathematical biosciences and engineering : MBE
|January 29, 2026
PubMed
概括

这项研究引入了一种新的数据增强方法,使用分区模型和深度学习来提高神经网络的准确性. 该方法提高了物理信息神经网络 (PINNs) 和非线性自回归 (NAR) 模型的预测性能.

关键词:
有关COVID-19的数据数学流行病学数学流行病学非线性自回归深度学习基于物理学的神经网络.

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