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基于信息熵理论的传染病风险分析与评估方法。

Risk analysis and assessment method for infectious diseases based on information entropy theory.

机构信息

Yunnan University of Finance and Economics, Kunming, 650221, China.

Yunnan Key Laboratory of Service Computing, Kunming, 650221, China.

出版信息

Sci Rep. 2024 Jul 23;14(1):16898. doi: 10.1038/s41598-024-67783-3.

DOI:10.1038/s41598-024-67783-3
PMID:39043801
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11266543/
Abstract

Infectious diseases risk is directly related to human life safety. After the COVID-19 pandemic, people have paid unprecedented attention to the risk of infectious diseases. Compared with treatment after the outbreak of the epidemic, identifying the influencing factors of infectious disease risk and quantitatively analyzing and assessing infectious disease risk before the outbreak of the epidemic plays an equally important role. This article focuses on the risk of irregular outbreaks of infectious diseases. On the one hand, a method based on information gain is proposed to calculate the weight of environmental factors directly related to infectious disease risk, to clarify the correlation between environmental factors and infectious disease risk. On the other hand, the risk calculation method based on risk weight number is proposed to calculate the risk level of different infectious diseases under the influence of specific environmental factors. Finally, the effectiveness and feasibility of the proposed method are verified through case analysis and discussion. By comparing it with other risk assessment methods, the advantages and disadvantages of the proposed method are demonstrated.

摘要

传染病风险直接关系到人类生命安全。在 COVID-19 大流行之后,人们对传染病风险的关注度空前提高。相较于疫情爆发后的治疗,在疫情爆发前识别传染病风险的影响因素并对传染病风险进行定量分析和评估同样重要。本文聚焦于传染病不规则爆发的风险。一方面,提出了一种基于信息增益的方法来计算与传染病风险直接相关的环境因素的权重,以明确环境因素与传染病风险之间的相关性。另一方面,提出了一种基于风险权重数的风险计算方法,以计算在特定环境因素影响下不同传染病的风险水平。最后,通过案例分析和讨论验证了所提出方法的有效性和可行性。通过与其他风险评估方法进行比较,展示了所提出方法的优缺点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/064c/11266543/4a3cf2cd01ec/41598_2024_67783_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/064c/11266543/714cef457f89/41598_2024_67783_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/064c/11266543/c77d21eb0dc6/41598_2024_67783_Fig2_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/064c/11266543/e1e613761796/41598_2024_67783_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/064c/11266543/1fb49102c307/41598_2024_67783_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/064c/11266543/4a3cf2cd01ec/41598_2024_67783_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/064c/11266543/714cef457f89/41598_2024_67783_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/064c/11266543/c77d21eb0dc6/41598_2024_67783_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/064c/11266543/79e4a6e50e4d/41598_2024_67783_Figa_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/064c/11266543/e1e613761796/41598_2024_67783_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/064c/11266543/1fb49102c307/41598_2024_67783_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/064c/11266543/4a3cf2cd01ec/41598_2024_67783_Fig5_HTML.jpg

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