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

Md Shahinoor Rahman; Kamal Chandra Paul; Md Mokhlesur Rahman; Jim Samuel; Jean-Claude Thill; Md Amjad Hossain; G G Md Nawaz Ali

doi:10.1016/j.scs.2023.104570

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

Sustain Cities Soc. 2023 Aug:95:104570. doi: 10.1016/j.scs.2023.104570. Epub 2023 Apr 11.

Authors

Md Shahinoor Rahman¹, Kamal Chandra Paul², Md Mokhlesur Rahman^{3

4}, Jim Samuel⁵, Jean-Claude Thill^{6

7}, Md Amjad Hossain⁸, G G Md Nawaz Ali⁹

Affiliations

¹ Department of Earth and Environmental Sciences, New Jersey City University, Jersey City, NJ, 07305, USA.
² Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.
³ The William States Lee College of Engineering, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.
⁴ Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, Khulna, 9203, Bangladesh.
⁵ E.J. Bloustein School of Planning & Public Policy, Rutgers University, NJ, 08901, USA.
⁶ Department of Geography and Earth Sciences, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.
⁷ School of Data Science, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.
⁸ Department of Accounting, Information Systems, and Finance, Emporia State University, Emporia, KS, 66801, USA.
⁹ Department of Computer Science and Information Systems, Bradley University, Peoria, IL, 61625, USA.

Abstract

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.

Keywords: COVID-19; Coronavirus; PVI; Pandemic vulnerability index; Resiliency; US cities.