Restricted accessResearch articleFirst published online 2023-9
Geographical detector-based assessment of multi-level explanatory powers of determinants on China’s medical-service resumption during the COVID-19 epidemic
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.
Abu-RayashADincerI (2020) Analysis of mobility trends during the COVID-19 coronavirus pandemic: exploring the impacts on global aviation and travel in selected cities. Energy Research & Social Science68: 101693.
2.
AnHSunX (2021) Impact of risk perception on migrant workers’ employment choice during the COVID-19 epidemic. The Chinese Economy54(6): 402–414.
3.
BaiLLuHHuH, et al. (2021) Evaluation of work resumption strategies after COVID-19 reopening in the Chinese city of Shenzhen: a mathematical modeling study. Public Health193: 17–22.
4.
China CDC (2021) Public platform of the 2019-nCov-infected pneumonia epidemic (in Chinese). Available at: http://2019ncov.chinacdc.cn/2019-nCoV/ (accessed 15 April 2021).
5.
DubeKNhamoGChikodziD (2021) COVID-19 pandemic and prospects for recovery of the global aviation industry. Journal of Air Transport Management92: 102022.
6.
FotheringhamASWongDWS (1991) The modifiable areal unit problem in multivariate statistical analysis. Environment and Planning A: Economy and Space23(7): 1025–1044.
7.
GeYZhangW-BWangJ, et al. (2021) Effect of different resumption strategies to flatten the potential COVID-19 outbreaks amid society reopens: a modeling study in China. BMC Public Health21(1): 604.
8.
GösslingSScottDHallCM (2021) Pandemics, tourism and global change: a rapid assessment of COVID-19. Journal of Sustainable Tourism29(1): 1–20.
9.
HeFShangXLingF, et al. (2021) A practice of using five-colour chart to guide the control of COVID-19 and resumption of work in Zhejiang Province, China. Scientific Reports11(1): 11317.
10.
HongYCaiGMoZ, et al. (2020) The impact of COVID-19 on tourist satisfaction with B&B in Zhejiang, China: an importance–performance analysis. International Journal of Environmental Research and Public Health17(10): 3747.
11.
HuMLinHWangJ, et al. (2021b) Risk of coronavirus disease 2019 transmission in train passengers: an epidemiological and modeling study. Clinical Infectious Diseases72(4): 604–610.
12.
HuBNingPQiuJet al. (2021a) Modeling the complete spatiotemporal spread of the COVID-19 epidemic in mainland China. International Journal of Infectious Diseases110: 247–257.
13.
HuBQiuJChenH, et al. (2020) First, second and potential third generation spreads of the COVID-19 epidemic in mainland China: an early exploratory study incorporating location-based service data of mobile devices. International Journal of Infectious Diseases96: 489–495.
14.
HuBZhangQTaoV, et al. (2022) Assessing work resumption in hospitals during the COVID-19 epidemic in China using multiscale geographically weighted regression. Transactions in GIS26(4): 2023–2040.
15.
HuangHYaoXAKrispJM, et al. (2021) Analytics of location-based big data for smart cities: opportunities, challenges, and future directions. Computers, Environment and Urban Systems90: 101712.
16.
JiangBYaoX (2006) Location-based services and GIS in perspective. Computers, Environment and Urban Systems30(6): 712–725.
17.
JiangBde RijkeC (2021) A power-law-based approach to mapping COVID-19 cases in the United States. Geo-spatial Information Science24(3): 333–339.
18.
KraemerMUGYangC-HGutierrezB, et al. (2020) The effect of human mobility and control measures on the COVID-19 epidemic in China. Science368(6490): 493–497.
19.
LaiJZhuJXieY, et al. (2022) Understanding China’s resumption of work and production during the critical period of COVlD-19 based on multi-source data. Tropical Gastroenterology: Official Journal of the Digestive Diseases Foundation26(2): 1062–1079.
20.
LalPKumarAKumarS, et al. (2020) The dark cloud with a silver lining: assessing the impact of the SARS COVID-19 pandemic on the global environment. The Science of the Total Environment732: 139297.
21.
LiLLiQHuangL, et al. (2020) Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: an insight into the impact of human activity pattern changes on air pollution variation. The Science of the Total Environment732: 139282.
22.
LiZZhangXYangK, et al. (2021) Urban and rural tourism under COVID-19 in China: research on the recovery measures and tourism development. Tourism Review76(4): 718–736.
23.
LiuKZhaoPWanD, et al. (2022) Using mobile phone big data to discover the spatial patterns of rural migrant workers’ return to work in China’s three urban agglomerations in the post-COVID-19 era. Environment and Planning B: Urban Analytics and City Science2022: 239980832110693.
24.
LiuQShaDLiuW, et al. (2020) Spatiotemporal patterns of COVID-19 Impact on human activities and environment in mainland China using nighttime light and air quality data. Remote Sensing12(10): 1576.
25.
LuoWJasiewiczJStepinskiT, et al. (2016) Spatial association between dissection density and environmental factors over the entire conterminous United States. Geophysical Research Letters43(2): 692–700.
26.
MuXYehAG-OZhangX (2021) The interplay of spatial spread of COVID-19 and human mobility in the urban system of China during the Chinese New Year. Environment and Planning B: Urban Analytics and City Science48(7): 1955–1971.
27.
MuhammadSLongXSalmanM (2020) COVID-19 pandemic and environmental pollution: A blessing in disguise?The Science of the Total Environment728: 138820.
28.
NathanMOvermanH (2020) Will coronavirus cause a big city exodus?Environment and Planning B: Urban Analytics and City Science47(9): 1537–1542.
29.
NorouziNZarazua de RubensGChoupanpieshehS, et al. (2020) When pandemics impact economies and climate change: Exploring the impacts of COVID-19 on oil and electricity demand in China. Energy Research & Social Science68: 101654.
30.
OpenshawS (1984) The Modifiable Areal Unit Problem. Norwich: Geo Books.
31.
ShaoZTangYHuangX, et al. (2021) Monitoring work resumption of Wuhan in the COVID-19 epidemic using daily nighttime light. Photogrammetric Engineering & Remote Sensing87(3): 195–204.
32.
ShawS-LSuiD (2021) Mapping COVID-19 in Space and Time: Understanding the Spatial and Temporal Dynamics of a Global Pandemic. Switzerland: Springer.
33.
SicardPDe MarcoAAgathokleousE, et al. (2020) Amplified ozone pollution in cities during the COVID-19 lockdown. The Science of the Total Environment735: 139542.
34.
TaoJFanMGuJ, et al. (2020) Satellite observations of the return-to-work over China during the period of COVID-19. Journal of Remote Sensing24(07): 824–836.
35.
TianHLiuYLiY, et al. (2020) An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science368(6491): 638–642.
36.
TianSFengRZhaoJ, et al. (2021) An analysis of the work resumption in China under the COVID-19 epidemic based on night time lights data. ISPRS International Journal of Geo-Information10(9): 614.
37.
WangJLiXChristakosG, et al. (2010) Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China. International Journal of Geographical Information Science24(1): 107–127.
38.
WangJZhangTFuB (2016) A measure of spatial stratified heterogeneity. Ecological Indicators67: 250–256.
39.
WangXTangSChenY, et al. (2020) When will be the resumption of work in Wuhan and its surrounding areas during COVID-19 epidemic? A data-driven network modeling analysis. Social Science & Medicine50(7): 969.
40.
XuXWangSDongJ, et al. (2020b) An analysis of the domestic resumption of social production and life under the COVID-19 epidemic. Plos One15(7): e0236387.
41.
XuBWangJLiZ, et al. (2021) Seasonal association between viral causes of hospitalised acute lower respiratory infections and meteorological factors in China: a retrospective study. The Lancet Planetary Health5(3): e154–e163.
42.
XuTAoMZhouX, et al. (2020a) China’s practice to prevent and control COVID-19 in the context of large population movement. Infectious Diseases of Poverty9(1): 115.
43.
YinQWangJRenZ, et al. (2019) Mapping the increased minimum mortality temperatures in the context of global climate change. Nature Communications10(1): 1–8.
44.
ZhangLLiuWHouK, et al. (2019) Air pollution exposure associates with increased risk of neonatal jaundice. Nature Communications10(1): 3741.
45.
ZhangWGeYLiuM, et al. (2021) Risk assessment of the step-by-step return-to-work policy in Beijing following the COVID-19 epidemic peak. Stochastic Environmental Research and Risk Assessment35(2): 481–498.
46.
ZhouYFengLZhangX, et al. (2021) Spatiotemporal patterns of the COVID-19 control measures impact on industrial production in Wuhan using time-series earth observation data. Sustainable Cities and Society75: 103388.
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