Consensus reaching-based decision model for assessing resilient urban public health safety ecosystem with social network analysis

• Published in: Journal of industrial information integration 2024-11, Vol.42, p.100716, Article 100716
• Main Authors: Wang, Zelin, Wang, Xiangbin, Wang, Weizhong, Deveci, Muhammet, Wu, Zengyuan, Pedrycz, Witold
• Format: Article
• Language: English
• Subjects: Consensus-reaching process; Emergency capacity assessment; Social network analysis; Urban public health development
• ISSN: 2452-414X
• DOI: 10.1016/j.jii.2024.100716

In 2021, United Nations released the "Creating Resilient Cities 2030 Project", which aims to strengthen urban resilience in developing and implementing disaster reduction strategies. Resilient cities are a new type of urban development model that emphasizes the ability of cities to resist natural disasters and social pressures, reduce losses, and allocate resources reasonably to quickly recover from disasters. With the frequent occurrence of public health and safety accidents, the concept of public health safety ecosystem has become increasingly prominent in the field of urban resilience. To effectively manage public health incidents and enhance emergency response capabilities, evaluating the urban public health safety ecosystem is essential. A consensus-based decision-making model that accounts for the social networks among experts to accurately assess urban public health emergency capacity is introduced. To ensure the objectivity of indicator weights, we build up a novel model to calculate the weight of indicators utilizing social network analysis and consensus-reaching process analysis of indicator evaluation value. An illustrative case study on public health emergency capacity in Luoding is presented. This research expands the framework for assessing resilience in urban systems and provides a methodology for improving urban public health and resilience, introducing a novel approach for evaluating the urban public health safety ecosystem through social network analysis.