College of Information Engineering, Shanghai Maritime University, Shanghai, China.
Shanghai Experimental School International Division, Shanghai, China.
Front Public Health. 2024 Mar 7;12:1339611. doi: 10.3389/fpubh.2024.1339611. eCollection 2024.
Metropolitan governance's efficacy is regularly gauged by its capability for public health preparedness, a critical component, particularly in the post-pandemic climate, as global cities reassess their mitigation abilities. This process has broader implications, curbing mortality rates and amplifying sustainability. Current methodologies for preparedness assessment lean primarily on either Subjective Evaluation-Based Assessment (SBA), predicated on experts' input on various capacity indicators, or they opt for Data-Based quantitative Assessments (DBA), chiefly utilizing public statistic data.
The manuscript discusses an urgent need for integrating both SBA and DBA to adequately measure Metropolitan Public Health Pandemics Preparedness (MPHPP), thus proposing a novel entropy-TOPSIS-IF model for comprehensive evaluation of MPHPP. Within this proposed model, experts' subjective communication is transformed into quantitative data via the aggregation of fuzzy decisions, while objective data is collected from public statistics sites. Shannon's entropy and TOPSIS methods are enacted on these data sets to ascertain the optimal performer after normalization and data isotropy.
The core contribution of the entropy-TOPSIS-IF model lies in its assessment flexibility, making it universally applicable across various contexts, regardless of the availability of expert decisions or quantitative data. To illustrate the efficacy of the entropy-TOPSIS-IF model, a numerical application is presented, examining three Chinese metropolises through chosen criteria according to the evaluations of three experts. A sensitivity analysis is provided to further affirm the stability and robustness of the suggested MPHPP evaluation model.
大都市治理的效能通常通过其公共卫生准备能力来衡量，这是一个关键组成部分，尤其是在后疫情时代，全球城市正在重新评估其缓解能力。这个过程具有更广泛的意义，可以降低死亡率并提高可持续性。目前的准备情况评估方法主要依赖于基于主观评估的评估（SBA），该方法基于专家对各种能力指标的意见，或者选择基于数据的定量评估（DBA），主要利用公共统计数据。
本文讨论了整合 SBA 和 DBA 以充分衡量大都市公共卫生大流行准备情况（MPHPP）的迫切需要，因此提出了一种新的熵-TOPSIS-IF 模型，用于综合评估 MPHPP。在这个提议的模型中，专家的主观沟通通过模糊决策的聚合转化为定量数据，而客观数据则从公共统计网站收集。使用香农熵和 TOPSIS 方法对这些数据集进行操作，以在归一化和数据各向同性后确定最佳执行者。
熵-TOPSIS-IF 模型的核心贡献在于其评估的灵活性，使其可以在各种情况下普遍适用，无论是否有专家决策或定量数据。为了说明熵-TOPSIS-IF 模型的有效性，提出了一个数值应用程序，根据三位专家的评估，通过选择的标准对三个中国大都市进行了检查。进行了敏感性分析，以进一步确认建议的 MPHPP 评估模型的稳定性和鲁棒性。
