Mapping the Accessibility of Medical Facilities of Wuhan during the COVID-19 Pandemic

Abstract

In December 2019, the coronavirus disease 2019 (COVID-19) pandemic attacked Wuhan, China. The city government soon strictly locked down the city, implemented a hierarchical diagnosis and treatment system, and took a series of unprecedented pharmaceutical and non-pharmaceutical measures. The residents’ access to the medical resources and the consequently potential demand–supply tension may determine effective diagnosis and treatment, for which travel distance and time are key indicators. Using the Application Programming Interface (API) of Baidu Map, we estimated the travel distance and time from communities to the medical facilities capable of treating COVID-19 patients, and we identified the service areas of those facilities as well. The results showed significant differences in service areas and potential loading across medical facilities. The accessibility of medical facilities in the peripheral areas was inferior to those in the central areas; there was spatial inequality of medical resources within and across districts; the amount of community healthcare centers was insufficient; some communities were underserved regarding walking distance; some medical facilities could be potentially overloaded. This study provides reference, in the context of Wuhan, for understanding the spatial aspect of medical resources and residents’ relevant mobility under the emergency regulation, and re-examining the coordination of emergency to improve future planning and utilization of medical facilities at various levels. The approach can facilitate policymakers to assess potential loading of medical facilities, identify low-accessibility areas, and deploy new medical facilities. It also implies that the accessibility analysis can be rapid and relevant even only with open-source data.

Accessibility calculation and equality evaluation of medical facilities for COVID-19 pandemic treatment: A case study of the Wuhan metropolitan development zone

“…The distribution of emergency treatment facilities during a pandemic was imbalanced. This matched the findings of previous research [ 51 ]. When the time limit was short (e.g., 10–15 min), the equality gap between various areas was larger.…”

Identifying Spatial Matching between the Supply and Demand of Medical Resource and Accessing Carrying Capacity: A Case Study of Shenzhen, China

“…Shenzhen is a cosmopolitan city on the southeast coast of China, and its population density is much higher than that of similar first-tier cities, such as Beijing, Guangzhou, and Shanghai [ 20 ]. Given Shenzhen’s rising economic level, growing number of permanent residents, and changing floating population, as well as its increasing elderly population, residents’ demand for medical resources is showing a rising and diversified trend.…”

Dynamic Demand Evaluation of COVID-19 Medical Facilities in Wuhan Based on Public Sentiment

“…The key to a healthy city is urban healthcare facilities, which are mostly evaluated in existing studies focusing on spatial accessibility, equity [ 73 , 74 , 75 ] and supply-demand balance of healthcare facilities [ 76 , 77 , 78 ]. The outbreak of COVID-19 has reduced the geographic accessibility of medical facilities [ 79 , 80 , 81 , 82 ], and the reduction is greater in the urban periphery than in the central city [ 83 ].…”