Geographical detector-based assessment of multi-level explanatory powers of determinants on China’s medical-service resumption during the COVID-19 epidemic

Author:

Hu Bisong1ORCID,Fu Sumeng1,Luo Jin1,Lin Hui1,Yin Qian2,Tao Vincent3,Jiang Bin4ORCID,Zuo Lijun5,Meng Yu5

Affiliation:

1. School of Geography and Environment, Jiangxi Normal University, Nanchang, China

2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

3. Wayz AI Technology Company Limited, Shanghai, China

4. Faculty of Engineering and Sustainable Development, Division of GIScience, University of Gävle, Gävle, Sweden

5. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

Abstract

Knowing the multi-level influences of determinants on medical-service resumptions is of great benefits to the policymaking for medical-service recovery at different levels of study units during the post-COVID-19 pandemic era. This article evaluated the hospital- and city-level resumptions of medical services in mainland China based on the data of location-based service (LBS) requests of mobile devices during the two time periods (December 2019 and from February 21 to March 18, 2020). We selected medical-service capacity, human movement, epidemic severity, and socioeconomic factors as the potential determinants on medical-service resumptions and then explicitly assessed their multi-level explanatory powers and the interactive effects of paired determinants using the geographical detector method. The results indicate that various determinants had different individual explanatory powers and interactive relationships/effects at different levels of medical-service resumptions. The current study provides a novel multi-level insight for assessing work resumption and individual/interactive influences of determinants, and considerable implications for regionalized recovery strategies of medical services.

Funder

Graduate Innovation Fund of Jiangxi Normal University, China

Science and Technology Major Project of Jiangxi Province, China

Science and Technology Major Project of Jiangxi Provincial Office of Education, China

National Natural Science Foundation of China

Henan Province and Key Laboratory of Spatiotemporal Perception and Intelligent processing and Ministry of Natural Resources, China

Publisher

SAGE Publications

Subject

Management, Monitoring, Policy and Law,Nature and Landscape Conservation,Urban Studies,Geography, Planning and Development,Architecture

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Calibrating Spatial Stratified Heterogeneity for Heavy-Tailed Distributed Data;Annals of the American Association of Geographers;2024-06-18

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