Who Gets the Flu? Individualized Validation of Influenza-like Illness in Urban Spaces
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 t...
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Veröffentlicht in: | International journal of environmental research and public health 2023-05, Vol.20 (10), p.5865 |
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description | 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. |
doi_str_mv | 10.3390/ijerph20105865 |
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Individualized Validation of Influenza-like Illness in Urban Spaces</title><source>MEDLINE</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central Open Access</source><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Zhong, Shiran ; Ma, Fenglong ; Gao, Jing ; Bian, Ling</creator><creatorcontrib>Zhong, Shiran ; Ma, Fenglong ; Gao, Jing ; Bian, Ling</creatorcontrib><description>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.</description><identifier>ISSN: 1660-4601</identifier><identifier>ISSN: 1661-7827</identifier><identifier>EISSN: 1660-4601</identifier><identifier>DOI: 10.3390/ijerph20105865</identifier><identifier>PMID: 37239591</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Decision trees ; Demographics ; Disease transmission ; Epidemics ; Health aspects ; Health risk assessment ; Health risks ; Humans ; Illnesses ; Impact analysis ; Infections ; Influenza ; Influenza, Human - epidemiology ; Medical research ; Medicine, Experimental ; Neural networks ; Outdoor air quality ; Pandemics ; Policy ; Population ; Population Health ; Prognosis ; Public spaces ; Regression analysis ; Signs and symptoms ; Support vector machines ; Urban areas ; Urban health ; Virus Diseases</subject><ispartof>International journal of environmental research and public health, 2023-05, Vol.20 (10), p.5865</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. 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Individualized Validation of Influenza-like Illness in Urban Spaces</title><title>International journal of environmental research and public health</title><addtitle>Int J Environ Res Public Health</addtitle><description>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. 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Individualized Validation of Influenza-like Illness in Urban Spaces</title><author>Zhong, Shiran ; Ma, Fenglong ; Gao, Jing ; Bian, Ling</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3565-ec91f3787999079dc240d1cbfdffef936aab1da2f2c86478d6e926eed1be3bc83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Decision trees</topic><topic>Demographics</topic><topic>Disease transmission</topic><topic>Epidemics</topic><topic>Health aspects</topic><topic>Health risk assessment</topic><topic>Health risks</topic><topic>Humans</topic><topic>Illnesses</topic><topic>Impact analysis</topic><topic>Infections</topic><topic>Influenza</topic><topic>Influenza, Human - epidemiology</topic><topic>Medical research</topic><topic>Medicine, Experimental</topic><topic>Neural networks</topic><topic>Outdoor air quality</topic><topic>Pandemics</topic><topic>Policy</topic><topic>Population</topic><topic>Population Health</topic><topic>Prognosis</topic><topic>Public spaces</topic><topic>Regression analysis</topic><topic>Signs and symptoms</topic><topic>Support vector machines</topic><topic>Urban areas</topic><topic>Urban health</topic><topic>Virus Diseases</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhong, Shiran</creatorcontrib><creatorcontrib>Ma, Fenglong</creatorcontrib><creatorcontrib>Gao, Jing</creatorcontrib><creatorcontrib>Bian, Ling</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>International journal of environmental research and public health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhong, Shiran</au><au>Ma, Fenglong</au><au>Gao, Jing</au><au>Bian, Ling</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Who Gets the Flu? 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subjects | Decision trees Demographics Disease transmission Epidemics Health aspects Health risk assessment Health risks Humans Illnesses Impact analysis Infections Influenza Influenza, Human - epidemiology Medical research Medicine, Experimental Neural networks Outdoor air quality Pandemics Policy Population Population Health Prognosis Public spaces Regression analysis Signs and symptoms Support vector machines Urban areas Urban health Virus Diseases |
title | Who Gets the Flu? Individualized Validation of Influenza-like Illness in Urban Spaces |
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