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COVID-19 大流行的时变 SUC 传染病模型的长时间分析。

Long-Time Analysis of a Time-Dependent SUC Epidemic Model for the COVID-19 Pandemic.

机构信息

Department of Mathematics, Korea University, Seoul 02841, Republic of Korea.

出版信息

J Healthc Eng. 2021 Oct 28;2021:5877217. doi: 10.1155/2021/5877217. eCollection 2021.

DOI:10.1155/2021/5877217
PMID:34745502
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8564194/
Abstract

In this study, we propose a time-dependent susceptible-unidentified infected-confirmed (tSUC) epidemic mathematical model for the COVID-19 pandemic, which has a time-dependent transmission parameter. Using the tSUC model with real confirmed data, we can estimate the number of unidentified infected cases. We can perform a long-time epidemic analysis from the beginning to the current pandemic of COVID-19 using the time-dependent parameter. To verify the performance of the proposed model, we present several numerical experiments. The computational test results confirm the usefulness of the proposed model in the analysis of the COVID-19 pandemic.

摘要

在这项研究中，我们提出了一个与时间有关的易感-未识别感染-确诊（tSUC）传染病数学模型，用于 COVID-19 大流行，该模型具有与时间有关的传播参数。使用带有实际确诊数据的 tSUC 模型，我们可以估计未识别感染病例的数量。我们可以使用时变参数对 COVID-19 大流行从开始到现在的长期流行情况进行分析。为了验证所提出模型的性能，我们展示了几个数值实验。计算测试结果证实了所提出模型在 COVID-19 大流行分析中的有用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/589c/8564194/3635a27d6802/JHE2021-5877217.008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/589c/8564194/3635a27d6802/JHE2021-5877217.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/589c/8564194/6f615151bfda/JHE2021-5877217.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/589c/8564194/f7152d649900/JHE2021-5877217.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/589c/8564194/b30b6810a7c5/JHE2021-5877217.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/589c/8564194/e3d17c61c5ab/JHE2021-5877217.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/589c/8564194/d666aa805586/JHE2021-5877217.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/589c/8564194/faecf8581f6b/JHE2021-5877217.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/589c/8564194/ab6d4d8079a4/JHE2021-5877217.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/589c/8564194/3635a27d6802/JHE2021-5877217.008.jpg

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Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases.基于非线性神经网络的新冠肺炎病例预测模型
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