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波兰新冠肺炎疫情下一波的数学建模与预测

Mathematical modeling and estimation for next wave of COVID-19 in Poland.

作者信息

Arti M K, Wilinski Antoni

机构信息

NSUT East Campus (AIACTR), New Delhi, India.

WSB University in Gdansk, Gdansk, Poland.

出版信息

Stoch Environ Res Risk Assess. 2022;36(9):2495-2501. doi: 10.1007/s00477-021-02119-5. Epub 2021 Nov 26.

DOI:10.1007/s00477-021-02119-5
PMID:34849102
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8624632/
Abstract

We investigate the problem of mathematical modeling of new corona virus (COVID-19) in Poland and tries to predict the upcoming wave. A Gaussian mixture model is proposed to characterize the COVID-19 disease and to predict a new / future wave of COVID-19. This prediction is very much needed to prepare for medical setup and continue with the upcoming program. Specifically, data related to the new confirmed cases of COVID-19 per day are considered, and then we attempt to predict the data and statistical activity. A close match between actual data and analytical data by using the Gaussian mixture model shows that it is a suitable model to present new cases of COVID-19. In addition, it is thought that there are N waves of COVID-19 and that information for each future wave is also present in current and previous waves as well. Using this concept, predictions of a future wave can be made.

摘要

我们研究了波兰新型冠状病毒（COVID-19）的数学建模问题，并试图预测即将到来的疫情高峰。提出了一种高斯混合模型来表征COVID-19疾病，并预测新的/未来的COVID-19疫情高峰。为医疗准备工作和后续计划的推进，这种预测非常必要。具体而言，我们考虑了与每日新增COVID-19确诊病例相关的数据，然后尝试预测这些数据和统计活动。通过使用高斯混合模型，实际数据与分析数据之间的紧密匹配表明，它是呈现COVID-19新病例的合适模型。此外，人们认为COVID-19会出现N波疫情，并且每一波未来疫情的信息也存在于当前和之前的疫情中。利用这一概念，可以对未来的疫情高峰进行预测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9288/8624632/f7a9256f5266/477_2021_2119_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9288/8624632/931e5e66f636/477_2021_2119_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9288/8624632/9f90f8c6ae7e/477_2021_2119_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9288/8624632/ec9cea0f77a9/477_2021_2119_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9288/8624632/15ff86a312fe/477_2021_2119_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9288/8624632/17753387fc4f/477_2021_2119_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9288/8624632/f7a9256f5266/477_2021_2119_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9288/8624632/931e5e66f636/477_2021_2119_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9288/8624632/9f90f8c6ae7e/477_2021_2119_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9288/8624632/ec9cea0f77a9/477_2021_2119_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9288/8624632/15ff86a312fe/477_2021_2119_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9288/8624632/17753387fc4f/477_2021_2119_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9288/8624632/f7a9256f5266/477_2021_2119_Fig6_HTML.jpg

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2
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Expert Syst Appl. 2021 Jun 15;172:114654. doi: 10.1016/j.eswa.2021.114654. Epub 2021 Feb 1.
3
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大流行相关不确定性对一氧化碳排放的不对称影响：来自十大污染国家的证据。
Stoch Environ Res Risk Assess. 2022;36(12):4103-4117. doi: 10.1007/s00477-022-02248-5. Epub 2022 Jul 16.
Pers Individ Dif. 2021 Feb 15;170:110419. doi: 10.1016/j.paid.2020.110419. Epub 2020 Oct 9.
4
Neural network powered COVID-19 spread forecasting model.基于神经网络的新冠疫情传播预测模型。
Chaos Solitons Fractals. 2020 Nov;140:110203. doi: 10.1016/j.chaos.2020.110203. Epub 2020 Aug 15.
5
COVID-19 interventions in some European countries induced bifurcations stabilizing low death states against high death states: An eigenvalue analysis based on the order parameter concept of synergetics.一些欧洲国家的新冠疫情防控措施导致了分岔现象，使低死亡状态相对于高死亡状态得以稳定：基于协同学序参量概念的特征值分析
Chaos Solitons Fractals. 2020 Nov;140:110194. doi: 10.1016/j.chaos.2020.110194. Epub 2020 Aug 11.
6
Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review.机器学习和人工智能在2019冠状病毒病（严重急性呼吸综合征冠状病毒2）大流行中的应用：综述
Chaos Solitons Fractals. 2020 Oct;139:110059. doi: 10.1016/j.chaos.2020.110059. Epub 2020 Jun 25.
7
Simulating the spread of COVID-19 a spatially-resolved susceptible-exposed-infected-recovered-deceased (SEIRD) model with heterogeneous diffusion.模拟2019冠状病毒病的传播：一个具有异质扩散的空间分辨易感-暴露-感染-康复-死亡（SEIRD）模型
Appl Math Lett. 2021 Jan;111:106617. doi: 10.1016/j.aml.2020.106617. Epub 2020 Jul 15.
8
Agent-based modelling for SARS-CoV-2 epidemic prediction and intervention assessment: A methodological appraisal.基于主体建模用于严重急性呼吸综合征冠状病毒2（SARS-CoV-2）疫情预测与干预评估：方法学评价
J Eval Clin Pract. 2020 Oct;26(5):1352-1360. doi: 10.1111/jep.13459. Epub 2020 Aug 21.
9
Mathematical modeling of COVID-19 fatality trends: Death kinetics law versus infection-to-death delay rule.COVID-19死亡趋势的数学建模：死亡动力学定律与感染至死亡延迟规则。
Chaos Solitons Fractals. 2020 Jul;136:109891. doi: 10.1016/j.chaos.2020.109891. Epub 2020 May 30.
10
Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries.动态干预控制 COVID-19 大流行：比较全球 16 个国家的多变量预测建模研究。
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