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

TL;DR
This paper introduces a hybrid approach combining knowledge-based and data-driven methods to develop a Pandemic Vulnerability Index for US cities, aiding in understanding and managing pandemic risks.
Contribution
It presents the PVI-CI index, classifies cities into vulnerability levels, and offers a blueprint for similar indices globally, enhancing pandemic preparedness.
Findings
Identified key variables influencing COVID-19 impact.
Classified cities into five vulnerability categories.
Provided insights on spatial clustering of vulnerabilities.
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 COVID-19 cases and fatalities. The study uses a multi-method approach to study the influence of variables, classified as demographic, socioeconomic, mobility and…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Data-Driven Disease Surveillance · COVID-19 Pandemic Impacts
