Evidence map›Paper›PMID 37065624›Full record

ArticleSustainable cities and society2023

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

Abstract read
In one paragraph

Article in Sustainable cities and society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Md Shahinoor RahmanDepartment of Earth and Environmental Sciences, New Jersey City University, Jersey City, NJ, 07305, USA.
Kamal Chandra PaulDepartment of Electrical and Computer Engineering, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.
Md Mokhlesur RahmanThe William States Lee College of Engineering, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.
Jim SamuelE.J. Bloustein School of Planning & Public Policy, Rutgers University, NJ, 08901, USA.
Jean-Claude ThillDepartment of Geography and Earth Sciences, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.
Md Amjad HossainDepartment of Accounting, Information Systems, and Finance, Emporia State University, Emporia, KS, 66801, USA.
G G Md Nawaz AliDepartment of Computer Science and Information Systems, Bradley University, Peoria, IL, 61625, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

CoronavirusCOVID-19Pandemic vulnerability indexPVIResiliencyUS cities

Identifiers

PMID37065624
PMCPMC10085879

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.