Suppr超能文献

巴西伯南布哥州对 COVID-19 的脆弱性:基于多标准决策辅助方法的地理空间评估。

Vulnerability to COVID-19 in Pernambuco, Brazil: A geospatial evaluation supported by multiple-criteria decision aid methodology.

机构信息

Department of Engineering, Federal Rural University of the Semi-Arid, Angicos, RN.

Department of Production Engineering, Federal University of Pernambuco, Recife, PE.

出版信息

Geospat Health. 2022 Jan 14;17(s1). doi: 10.4081/gh.2022.1000.

Abstract

The paper presents an innovative application to identify areas vulnerable to coronavirus disease 2019 (COVID-19) considering a combination of spatial analysis and a multi-criteria learning approach. We applied this methodology in the state of Pernambuco, Brazil identifying vulnerable areas by considering a set of determinants and risk factors for COVID-19, including demographic, economic and spatial characteristics and the number of human COVID-19 infections. Examining possible patterns over a set number of days taking the number of cases recorded, we arrived at a set of compatible decision rules to explain the relation between risk factors and COVID-19 cases. The results reveal why certain municipalities are critically vulnerable to COVID-19 highlighting locations for which knowledge can be gained about environmental factors.

摘要

本文提出了一种创新的应用方法,通过空间分析和多准则学习方法的结合,来识别易感染 2019 年冠状病毒病(COVID-19)的区域。我们在巴西伯南布哥州应用了这种方法,通过考虑一组 COVID-19 的决定因素和风险因素,包括人口、经济和空间特征以及人类 COVID-19 感染的数量,来确定易感染区域。通过检查一定天数内记录的病例数量,我们得出了一组兼容的决策规则,以解释风险因素和 COVID-19 病例之间的关系。结果揭示了为什么某些城市易受 COVID-19 的严重影响,突出了需要了解环境因素的位置。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验