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Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach

Cities become mission-critical zones during pandemics and it is vital to develop a better understanding of the factors that are associated with infection levels. The COVID-19 pandemic has impacted many cities severely; however, there is significant variance in its impact across cities. Pandemic infe...

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Autores principales: Rahman, Md. Shahinoor, Paul, Kamal Chandra, Rahman, Md. Mokhlesur, Samuel, Jim, Thill, Jean-Claude, Hossain, Md. Amjad, Ali, G. G. Md. Nawaz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10085879/
https://www.ncbi.nlm.nih.gov/pubmed/37065624
http://dx.doi.org/10.1016/j.scs.2023.104570
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author Rahman, Md. Shahinoor
Paul, Kamal Chandra
Rahman, Md. Mokhlesur
Samuel, Jim
Thill, Jean-Claude
Hossain, Md. Amjad
Ali, G. G. Md. Nawaz
author_facet Rahman, Md. Shahinoor
Paul, Kamal Chandra
Rahman, Md. Mokhlesur
Samuel, Jim
Thill, Jean-Claude
Hossain, Md. Amjad
Ali, G. G. Md. Nawaz
author_sort Rahman, Md. Shahinoor
collection PubMed
description Cities become mission-critical zones during pandemics and it is vital to develop a better understanding of the factors that are associated with infection levels. The COVID-19 pandemic has impacted many cities severely; however, there is significant variance in its impact across cities. Pandemic infection levels are associated with inherent features of cities (e.g., population size, density, mobility patterns, socioeconomic condition, and health & environment), which need to be better understood. Intuitively, the infection levels are expected to be higher in big urban agglomerations, but the measurable influence of a specific urban feature is unclear. The present study examines 41 variables and their potential influence on the incidence of COVID-19 infection cases. The study uses a multi-method approach to study the influence of variables, classified as demographic, socioeconomic, mobility and connectivity, urban form and density, and health and environment dimensions. This study develops an index dubbed the pandemic vulnerability index at city level (PVI-CI) for classifying the pandemic vulnerability levels of cities, grouping them into five vulnerability classes, from very high to very low. Furthermore, clustering and outlier analysis provides insights on the spatial clustering of cities with high and low vulnerability scores. This study provides strategic insights into levels of influence of key variables upon the spread of infections, along with an objective ranking for the vulnerability of cities. Thus, it provides critical wisdom needed for urban healthcare policy and resource management. The calculation method for the pandemic vulnerability index and the associated analytical process present a blueprint for the development of similar indices for cities in other countries, leading to a better understanding and improved pandemic management for urban areas, and more resilient planning for future pandemics in cities across the world.
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spelling pubmed-100858792023-04-11 Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach Rahman, Md. Shahinoor Paul, Kamal Chandra Rahman, Md. Mokhlesur Samuel, Jim Thill, Jean-Claude Hossain, Md. Amjad Ali, G. G. Md. Nawaz Sustain Cities Soc Article Cities become mission-critical zones during pandemics and it is vital to develop a better understanding of the factors that are associated with infection levels. The COVID-19 pandemic has impacted many cities severely; however, there is significant variance in its impact across cities. Pandemic infection levels are associated with inherent features of cities (e.g., population size, density, mobility patterns, socioeconomic condition, and health & environment), which need to be better understood. Intuitively, the infection levels are expected to be higher in big urban agglomerations, but the measurable influence of a specific urban feature is unclear. The present study examines 41 variables and their potential influence on the incidence of COVID-19 infection cases. The study uses a multi-method approach to study the influence of variables, classified as demographic, socioeconomic, mobility and connectivity, urban form and density, and health and environment dimensions. This study develops an index dubbed the pandemic vulnerability index at city level (PVI-CI) for classifying the pandemic vulnerability levels of cities, grouping them into five vulnerability classes, from very high to very low. Furthermore, clustering and outlier analysis provides insights on the spatial clustering of cities with high and low vulnerability scores. This study provides strategic insights into levels of influence of key variables upon the spread of infections, along with an objective ranking for the vulnerability of cities. Thus, it provides critical wisdom needed for urban healthcare policy and resource management. The calculation method for the pandemic vulnerability index and the associated analytical process present a blueprint for the development of similar indices for cities in other countries, leading to a better understanding and improved pandemic management for urban areas, and more resilient planning for future pandemics in cities across the world. Elsevier Ltd. 2023-08 2023-04-11 /pmc/articles/PMC10085879/ /pubmed/37065624 http://dx.doi.org/10.1016/j.scs.2023.104570 Text en © 2023 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Rahman, Md. Shahinoor
Paul, Kamal Chandra
Rahman, Md. Mokhlesur
Samuel, Jim
Thill, Jean-Claude
Hossain, Md. Amjad
Ali, G. G. Md. Nawaz
Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach
title Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach
title_full Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach
title_fullStr Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach
title_full_unstemmed Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach
title_short Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach
title_sort pandemic vulnerability index of us cities: a hybrid knowledge-based and data-driven approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10085879/
https://www.ncbi.nlm.nih.gov/pubmed/37065624
http://dx.doi.org/10.1016/j.scs.2023.104570
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