County-level prioritization for managing the Covid-19 pandemic: a systematic unsupervised learning approach

• Published in: Journal of systems and information technology Vol. 26; no. 2; pp. 276 - 309
• Main Authors: Hettiarachchi, Charitha Sasika, Sun, Nanfei, Le, Trang Minh Quynh, Saleem, Naveed
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Publishing Limited 07-05-2024; Emerald Group Publishing Limited
• Subjects: Analytic hierarchy process; Clustering; Coronaviruses; COVID-19; Epidemics; Hierarchies; Multiple criterion; Outbreaks; Pandemics; Research projects; Unsupervised learning; Viral diseases; United States > US; Italy; Pennsylvania; Fuzzy C-Means; K-Means clustering; Covid-19; County-level prioritization; Risk prioritization; Analytic hierarchy process
• ISSN: 1328-7265; 1758-8847
• DOI: 10.1108/JSIT-02-2023-0027

Purpose
The COVID-19 pandemic has posed many challenges in almost all sectors around the globe. Because of the pandemic, government entities responsible for managing health-care resources face challenges in managing and distributing their limited and valuable health resources. In addition, severe outbreaks may occur in a small or large geographical area. Therefore, county-level preparation is crucial for officials and organizations who manage such disease outbreaks. However, most COVID-19-related research projects have focused on either state- or country-level. Only a few studies have considered county-level preparations, such as identifying high-risk counties of a particular state to fight against the COVID-19 pandemic. Therefore, the purpose of this research is to prioritize counties in a state based on their COVID-19-related risks to manage the COVID outbreak effectively.
Design/methodology/approach
In this research, the authors use a systematic hybrid approach that uses a clustering technique to group counties that share similar COVID conditions and use a multi-criteria decision-making approach – the analytic hierarchy process – to rank clusters with respect to the severity of the pandemic. The clustering was performed using two methods, k-means and fuzzy c-means, but only one of them was used at a time during the experiment.
Findings
The results of this study indicate that the proposed approach can effectively identify and rank the most vulnerable counties in a particular state. Hence, state health resources managing entities can identify counties in desperate need of more attention before they allocate their resources and better prepare those counties before another surge.
Originality/value
To the best of the authors’ knowledge, this study is the first to use both an unsupervised learning approach and the analytic hierarchy process to identify and rank state counties in accordance with the severity of COVID-19.