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新冠疫情传播模型：基于对策放松情景的机器学习预测。

Modelization of Covid-19 pandemic spreading: A machine learning forecasting with relaxation scenarios of countermeasures.

机构信息

Mobility and Modeling Laboratory, Faculty of Sciences and Techniques, Hassan 1(st) University, 26 100 Settat, Morocco.

ISPITS - Higher Institute of Nursing and Health Techniques, Ministry of Health, 40 000 Marrakech, Morocco.

出版信息

J Infect Public Health. 2021 Apr;14(4):468-473. doi: 10.1016/j.jiph.2021.01.004. Epub 2021 Jan 12.

DOI:10.1016/j.jiph.2021.01.004
PMID:33743367
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7834115/
Abstract

BACKGROUND & OBJECTIVE: Mathematical modeling is the most scientific technique to understand the evolution of natural phenomena, including the spread of infectious diseases. Therefore, these modeling tools have been widely used in epidemiology for predicting risks and decision-making processes. The purpose of this paper is to provide an effective mathematical model for predicting the spread of Covid-19 pandemic.

METHODS

Our mathematical model is performed according to a SIDR model for infectious diseases. Epidemiological data from four countries; Belgium, Morocco, Netherlands and Russia, are used to validate this model. Also, we have evaluated the efficiency of Morocco's Covid-19 countermeasures and simulated the different relaxation plans in order to predict the effects of relaxation countermeasures.

RESULTS AND CONCLUSIONS

In this paper, we developed and validated a new way of data aggregation, modeling and interpretation to predict the spread of Covid-19, evaluate the efficiency of countermeasures and suggest potential scenarios. Our results will be used to keep the spread of Covid-19 under control in the world.

摘要

背景与目的

数学建模是理解自然现象演变的最科学技术，包括传染病的传播。因此，这些建模工具已广泛应用于流行病学中，用于预测风险和决策过程。本文旨在为预测新冠疫情的传播提供一种有效的数学模型。

方法

我们的数学模型是根据传染病的 SIDR 模型进行的。使用来自四个国家（比利时、摩洛哥、荷兰和俄罗斯）的流行病学数据对该模型进行验证。此外，我们评估了摩洛哥新冠疫情防控措施的有效性，并模拟了不同的放松计划，以预测放松防控措施的效果。

结果与结论

本文开发并验证了一种新的数据聚合、建模和解释方法，用于预测新冠疫情的传播，评估防控措施的有效性，并提出潜在的情景。我们的结果将用于在全球范围内控制新冠疫情的传播。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b570/7834115/cfe3773c9d22/gr12_lrg.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b570/7834115/b44879bf9bbf/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b570/7834115/2ccdd632c523/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b570/7834115/41ddb4359a86/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b570/7834115/0e544e99fc90/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b570/7834115/087fb5d59ad3/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b570/7834115/92ff03e59ba8/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b570/7834115/07ae71c28205/gr7_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b570/7834115/730d8df6388d/gr8_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b570/7834115/fde78bd5c0ce/gr9_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b570/7834115/0ed354c13c81/gr10_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b570/7834115/a5f2f61450f8/gr11_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b570/7834115/cfe3773c9d22/gr12_lrg.jpg

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