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运用犹豫模糊多准则决策方法识别新冠病毒传播中的主要风险因素

Identification of dominant risk factor involved in spread of COVID-19 using hesitant fuzzy MCDM methodology.

作者信息

Ghorui Neha, Ghosh Arijit, Mondal Sankar Prasad, Bajuri Mohd Yazid, Ahmadian Ali, Salahshour Soheil, Ferrara Massimiliano

机构信息

Department of Mathematics, Prasanta Chandra Mahalanobis Mahavidyalaya, Kolkata, West Bengal, India.

Department of Mathematics, St. Xavier's College (Autonomous), Kolkata, West Bengal, India.

出版信息

Results Phys. 2021 Feb;21:103811. doi: 10.1016/j.rinp.2020.103811. Epub 2021 Jan 7.

Abstract

The outburst of the pandemic Coronavirus disease since December 2019, has severely impacted the health and economy worldwide. The epidemic is spreading fast through various means, as the virus is very infectious. Medical science is exploring a vaccine, only symptomatic treatment is possible at the moment. To contain the virus, it is required to categorize the risk factors and rank those in terms of contagion. This study aims to evaluate risk factors involved in the spread of COVID-19 and to rank them. In this work, we applied the methodology namely, Fuzzy Analytic Hierarchy Process (FAHP) to find out the weights and finally Hesitant Fuzzy Sets (HFS) with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to identify the major risk factor. The results showed that "long duration of contact with the infected person" the most significant risk factor, followed by "spread through hospitals and clinic" and "verbal spread". We showed the appliance of the Multi Criteria Decision Making (MCDM) tools in evaluation of the most significant risk factor. Moreover, we conducted sensitivity analysis.

摘要

自2019年12月以来大流行的冠状病毒病爆发,严重影响了全球的健康和经济。由于该病毒具有很强的传染性,疫情正通过各种途径迅速蔓延。医学正在探索疫苗,目前只能进行对症治疗。为了控制病毒,需要对风险因素进行分类并根据传染性对其进行排序。本研究旨在评估新冠病毒传播所涉及的风险因素并对其进行排序。在这项工作中,我们应用了模糊层次分析法(FAHP)来确定权重,最后应用犹豫模糊集(HFS)和理想解相似排序法(TOPSIS)来识别主要风险因素。结果表明,“与感染者长时间接触”是最显著的风险因素,其次是“通过医院和诊所传播”以及“口头传播”。我们展示了多准则决策(MCDM)工具在评估最显著风险因素中的应用。此外,我们还进行了敏感性分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/41f2/7833077/0bf87a5ebcc9/gr1_lrg.jpg

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