Selecting Emergency Health Logistics Cities During Public-Health Crises: Three-Dimensional TOPSIS Framework

Abstract

Public-health crises, like the COVID-19 pandemic, can lead to logistics supply issues as a result of measures such as city lockdowns. This study introduces an evaluation framework that identifies emergency health logistics cities (EHLCs) to ensure a stable and quick logistics supply to nearby cities. The main factors considered were public-health risks, urban accessibility, and logistical support. Convolution-based infection risk calculations, extreme-value modeling, spatial syntax analysis, and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method were used to quantitatively assess cities’ suitability as EHLCs. Validating the framework with 2020 data from Chinese cities showed that our approach outperformed traditional methods, offering broader service coverage and a more reliable material supply during public-health crises. The findings recommend selecting nonprovincial capitals as EHLCs, reducing dependence on major urban centers, and improving resource distribution equity. This framework offers a robust, data-driven tool for optimizing emergency logistics systems, enhancing risk mitigation and efficiency under changing conditions.