Rahman Md Shahinoor, Paul Kamal Chandra, Rahman Md Mokhlesur, Samuel Jim, Thill Jean-Claude, Hossain Md Amjad, Ali G G Md Nawaz
Department of Earth and Environmental Sciences, New Jersey City University, Jersey City, NJ, 07305, USA.
Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.
Sustain Cities Soc. 2023 Aug;95:104570. doi: 10.1016/j.scs.2023.104570. Epub 2023 Apr 11.
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.
在疫情期间，城市成为关键任务区域，深入了解与感染水平相关的因素至关重要。新冠疫情对许多城市造成了严重影响；然而，其在不同城市的影响存在显著差异。疫情感染水平与城市的固有特征（如人口规模、密度、流动模式、社会经济状况以及健康与环境）相关，需要对此有更深入的了解。直观地说，大城市群的感染水平预计会更高，但特定城市特征的可衡量影响尚不清楚。本研究考察了41个变量及其对新冠感染病例发生率的潜在影响。该研究采用多方法来研究这些变量的影响，这些变量分为人口统计学、社会经济、流动与连通性、城市形态与密度以及健康与环境维度。本研究开发了一个名为城市层面疫情脆弱性指数（PVI - CI）的指标，用于对城市的疫情脆弱性水平进行分类，将它们分为从非常高到非常低的五个脆弱性类别。此外，聚类和离群值分析提供了关于高脆弱性得分和低脆弱性得分城市的空间聚类的见解。本研究提供了关于关键变量对感染传播影响程度的战略见解，以及城市脆弱性的客观排名。因此，它为城市医疗政策和资源管理提供了所需的关键智慧。疫情脆弱性指数的计算方法和相关分析过程为其他国家城市开发类似指数提供了蓝图，有助于更好地理解和改善城市地区的疫情管理，并为全球城市未来的疫情制定更具弹性的规划。
