Assessing work resumption in hospitals during the COVID‐19 epidemic in China using multiscale geographically weighted regression

Abstract

The resumption of work and production is one of the key issues during the novel coronavirus (COVID‐19) post‐epidemic phase. We used location‐based service data of mobile devices to assess the work resumption of 22,098 hospitals in mainland China. The multiscale influences of the determinants on work resumption in hospitals, including medical‐service capacity, human movement, and epidemic severity, were examined using the multiscale geographically weighted regression technique. This study provides a novel insight into the assessment of work resumption in hospitals and its determinants, and is flexible to be extended to evaluate the work resumption of other industries. The findings can introduce helpful information for other countries to implement the strategies of work recovery during the post‐epidemic phase.

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

“…The LBS data used in this study were provided by Wayz Inc., Shanghai, China. The all-day LBS requests from over 80% of mobile devices supported by the three telecommunications operators in China are recorded with high-resolution locations, and the raw data collection is implemented every 2 h ( Hu et al, 2020 , 2021a , 2022 ). Note that individual mobile device with multiple LBS requests at a certain location is counted just once for a specific period during the process of data collection.…”

“…A resumption rate, defined in a space-time domain, was used to be representative of the spatiotemporal medical-service resumption situations in hospitals ( Hu et al, 2022 ). It indicates the ratio of the number of daily visits in a specific hospital s at a given date t during the epidemic to that prior to the epidemic.…”

“…The epidemic data of daily new confirmed cases included the spatial information of their residential districts and were associated with individual hospitals according to location information based on the geofencing technology. We selected the daily new confirmed cases within the 3 km buffer around hospitals to be a proxy variable of the epidemic severity ( Hu et al, 2022 ). Similarly, the data of daily new cases around hospitals were averagely aggregated into cities by date.…”

“…Another better alternative is the location-based service (LBS) data of mobile devices ( Huang et al, 2021 ; Jiang and Yao, 2006 ), which help indicate explicit trajectories of human movement with high-resolution spatiotemporal information. The LBS data of mobile devices had been successfully applied to explore the spatiotemporal epidemic spread associated with population flow ( Hu et al, 2020 , 2021a ) and to assess the work resumption in hospitals during the epidemic ( Hu et al, 2022 ).…”

“…Various strategies and measures for the work resumption and the corresponding risks were explicitly evaluated (Bai et al, 2021; Ge et al, 2021; Wang et al, 2020; Zhang et al, 2021). Moreover, the work resumption of a specific industry (e.g., medical services) and the influences of determinants can be assessed (Hu et al, 2022).…”