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谁会得流感?城市空间中流感样疾病的个体化验证。

Who Gets the Flu? Individualized Validation of Influenza-like Illness in Urban Spaces.

机构信息

Department of Geography, University of Western Ontario, London, ON N6A 3K7, Canada.

College of Information Sciences and Technology, Pennsylvania State University, University Park, State College, PA 16802, USA.

出版信息

Int J Environ Res Public Health. 2023 May 18;20(10):5865. doi: 10.3390/ijerph20105865.

Abstract

Urban dwellers are exposed to communicable diseases, such as influenza, in various urban spaces. Current disease models are able to predict health outcomes at the individual scale but are mostly validated at coarse scales due to the lack of fine-scaled ground truth data. Further, a large number of transmission-driving factors have been considered in these models. Because of the lack of individual-scaled validations, the effectiveness of factors at their intended scale is not substantiated. These gaps significantly undermine the efficacy of the models in assessing the vulnerability of individuals, communities, and urban society. The objectives of this study are twofold. First, we aim to model and, most importantly, validate influenza-like illness (ILI) symptoms at the individual scale based on four sets of transmission-driving factors pertinent to home-work space, service space, ambient environment, and demographics. The effort is supported by an ensemble approach. For the second objective, we investigate the effectiveness of the factor sets through an impact analysis. The validation accuracy reaches 73.2-95.1%. The validation substantiates the effectiveness of factors pertinent to urban spaces and unveils the underlying mechanism that connects urban spaces and population health. With more fine-scaled health data becoming available, the findings of this study may see increasing value in informing policies that improve population health and urban livability.

摘要

城市居民在各种城市空间中接触到传染病,如流感。当前的疾病模型能够预测个体尺度上的健康结果,但由于缺乏细粒度的地面实况数据,大多数模型都是在粗尺度上验证的。此外,这些模型中已经考虑了大量的传播驱动因素。由于缺乏个体尺度的验证,这些因素在其预期尺度上的有效性无法得到证实。这些差距极大地削弱了这些模型在评估个体、社区和城市社会脆弱性方面的效力。本研究的目的有两个。首先,我们旨在基于与家庭-工作空间、服务空间、环境和人口统计学相关的四组传播驱动因素,对个体尺度上的流感样疾病(ILI)症状进行建模,最重要的是进行验证。该研究采用了集成方法。其次,我们通过影响分析研究了因素集的有效性。验证准确率达到 73.2-95.1%。验证证实了与城市空间相关的因素的有效性,并揭示了连接城市空间和人口健康的潜在机制。随着更多细粒度的健康数据的出现,本研究的发现可能会在为改善人口健康和城市宜居性的政策提供信息方面变得越来越有价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/386d/10218228/eb1d1772d219/ijerph-20-05865-g001.jpg

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