Geographically varying relationships between population flows from Wuhan and COVID-19 cases in Chinese cities

Autor: Gang Xu, Wenwu Wang, Dandan Lu, Binbin Lu, Kun Qin, Limin Jiao
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Geo-spatial Information Science, Vol 0, Iss 0, Pp 1-11 (2021)
Druh dokumentu: article
ISSN: 1009-5020
1993-5153
10095020
DOI: 10.1080/10095020.2021.1977093
Popis: The COVID-19 epidemic widely spread across China from Wuhan, Hubei Province, because of huge migration before 2020 Chinese New Year. Previous studies demonstrated that population outflows from Wuhan determined COVID-19 cases in other cities but neglected spatial heterogeneities of their relationships. Here, we use Geographically Weighted Regression (GWR) model to investigate the spatially varying influences of outflows from Wuhan. Overall, the GWR model increases explanatory ability of outflows from Wuhan by 20%, with the adjusted R2 increasing from ~0.6 of Ordinary Least Squares (OLS) models to ~0.8 of GWR models. The coefficient between logarithmic of outflows from Wuhan and COVID-19 cases in other cities is generally less than 1. The sub-linear scaling relationship indicates the increasing returns of outflows was restrained, proving the epidemic was efficiently controlled outside Hubei at the beginning without obvious local transmissions. Coefficients in GWR models vary in cities. Not only cities around Wuhan but also cities having close connections with Wuhan experienced higher coefficients, showing a higher vulnerability of these cities. The secondary or multi-level transmission networks deserve to be further explored to fully uncover influences of migrations on the COVID-19 pandemic.
Databáze: Directory of Open Access Journals
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