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2020年4月16日至5月16日使用地理信息系统(GIS)绘制的冠状病毒病脆弱性地图。

Coronavirus disease vulnerability map using a geographic information system (GIS) from 16 April to 16 May 2020.

作者信息

Razavi-Termeh Seyed Vahid, Sadeghi-Niaraki Abolghasem, Choi Soo-Mi

机构信息

Geoinformation Tech. Center of Excellence, Factulty of Geomatics, K.N. Toosi University of Technology, Tehran, Iran.

Dept. of Computer Science and Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, Republic of Korea.

出版信息

Phys Chem Earth (2002). 2022 Jun;126:103043. doi: 10.1016/j.pce.2021.103043. Epub 2021 Jun 16.

Abstract

In recent months, the world has been affected by the infectious coronavirus disease and Iran is one of the most affected countries. The Iranian government's health facilities for an urgent investigation of all provinces do not exist simultaneously. There is no management tool to identify the vulnerabilities of Iranian provinces in prioritizing health services. The aim of this study was to prepare a coronavirus vulnerability map of Iranian provinces using geographic information system (GIS) to monitor the disease. For this purpose, four criteria affecting coronavirus, including population density, percentage of older people, temperature, and humidity, were prepared in the GIS. A multiscale geographically weighted regression (MGWR) model was used to determine the vulnerability of coronavirus in Iran. An adaptive neuro-fuzzy inference system (ANFIS) model was used to predict vulnerability in the next two months. Results indicated that, population density and older people have a more significant impact on coronavirus in Iran. Based on MGWR models, Tehran, Mazandaran, Gilan, and Alborz provinces were more vulnerable to coronavirus in February and March. The ANFIS model findings showed that West Azerbaijan, Zanjan, Fars, Yazd, Semnan, Sistan and Baluchistan, and Tehran provinces were more vulnerable in April and May.

摘要

近几个月来,世界受到传染性冠状病毒病的影响,伊朗是受影响最严重的国家之一。伊朗政府的卫生设施无法同时对所有省份进行紧急调查。在确定伊朗各省在优先提供卫生服务方面的脆弱性时,没有管理工具。本研究的目的是利用地理信息系统(GIS)绘制伊朗各省的冠状病毒脆弱性地图,以监测该疾病。为此,在GIS中准备了影响冠状病毒的四个标准,包括人口密度、老年人百分比、温度和湿度。使用多尺度地理加权回归(MGWR)模型来确定伊朗冠状病毒的脆弱性。使用自适应神经模糊推理系统(ANFIS)模型预测未来两个月的脆弱性。结果表明,人口密度和老年人对伊朗的冠状病毒影响更为显著。基于MGWR模型,德黑兰、马赞德兰、吉兰和阿尔伯兹省在2月和3月更容易受到冠状病毒的影响。ANFIS模型的结果显示,西阿塞拜疆、赞詹、法尔斯、亚兹德、塞姆南、锡斯坦和俾路支斯坦以及德黑兰省在4月和5月更容易受到影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e633/9133353/2edd77cac9a3/gr1_lrg.jpg

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