Evaluating the metropolitan public health preparedness for pandemics using entropy-Topsis-IF

Datos Bibliográficos

ID22078161
AutoresJin Liu (0000-0001-5816-2422, Shanghai Maritime University), Allen Wood Liu, Xingye Li (0000-0002-5727-8877, Shanghai Maritime University), Hui Li (0000-0001-9355-1116, Education University of Hong Kong, autor de correspondencia), Wenwei Luo (0000-0003-0014-9799, Shanghai Normal University), Wei Chen (0000-0002-9419-148X, Shanghai International Studies University)
Año2024
Volumen12
Páginas1339611-1339611
Fecha de publicación2024-03-07
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaFrontiers in Public Health (JOURNAL)
Identificadores de la revistaISSN: 2296-2565 • E-ISSN: 2296-2565
EditorialFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2024.1339611
PMID38515601
OpenAlexW4392557974
IdiomaEN
Citas recibidas2
Referencias citadas37

Introduction: 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. Methods: 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. Results and discussion: 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

Business · Economics · Geography · Metropolitan area · Operations research · Preparedness · TOPSIS · Computer Science · COVID-19 epidemiological studies · Disaster Response and Management · Engineering · Viral Infections and Outbreaks Research