METHODS article
Front. Public Health
Sec. Disaster and Emergency Medicine
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1609641
This article is part of the Research TopicInnovative Strategies for Urban Public Health Resilience in Crisis SituationsView all 10 articles
Development of a data-driven urban immunity assessment model: Providing a new benchmark for urban governance under public health emergencies
Provisionally accepted
- 1Nanjing Forestry University, Nanjing, China
- 2Umeå University, Umeå, Västerbotten, Sweden
- 3Columbia University, New York City, New York, United States
- 4Hong Kong Polytechnic University, Kowloon, Hong Kong, SAR China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Public health emergencies (PHEs) pose significant challenges to global urban governance systems, necessitating the establishment of more efficient and dynamically adaptive response mechanisms.Numerous cases indicate that current urban governance still faces the risk of systemic failure under PHE shocks, leading to severe socio-economic consequences. Existing studies, based on theories such as resilience, emergency management, and risk management, primarily employ traditional statistical modeling or single-discipline approaches to explore improvement pathways. However, they fall short in cross-system and multi-agent coordination mechanisms, as well as data-driven intelligent optimization. Therefore, this project draws inspiration from the principles of the human immune system, introduces the concept of urban immunity to characterize the level of urban governance under PHEs, and follows the approach of "feature decoding → mechanism analysis → spatiotemporal measurement → trend prediction → model optimization → decision output". It refines the theoretical framework of urban immunity, analyzes urban immune response mechanisms, develops an immunity indicator system, assesses the spatiotemporal patterns of urban immunity, and builds a decision-making model using intelligent optimization methods to generate optimized solutions for different scenarios. Ultimately, the project aims to establish a data-driven, evidence-based decision-making approach. This project seeks to provide a more systematic and operational theoretical framework for urban public health governance while promoting the digital and intelligent transformation of public health management, thereby enhancing PHE prevention and control capabilities.
Keywords: Public health emergencies management, Urban immunity, machine learning, Assessment system, Optimization model
Received: 11 Apr 2025; Accepted: 14 May 2025.
Copyright: © 2025 Cui, Cao, Qin and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Peng Cui, Nanjing Forestry University, Nanjing, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.