Gravitational and Autoregressive Analysis Spatial Diffusion of COVID-19 in Hubei Province, China
Yanguang Chen, Yajing Li, Yuqing Long, Shuo Feng

TL;DR
This study models the spatial diffusion of COVID-19 in Hubei using gravity and spatial auto-regression models, revealing key factors influencing spread and the effectiveness of control measures.
Contribution
It introduces a local gravity model based on power law decay to effectively describe COVID-19 diffusion patterns in Hubei.
Findings
The local gravity model fits COVID-19 diffusion data well.
Wuhan's direct transmission dominates early spread.
Spatial isolation measures significantly reduced transmission.
Abstract
The spatial diffusion of epidemic disease follows distance decay law in geography, but different diffusion processes may be modeled by different mathematical functions under different spatio-temporal conditions. This paper is devoted to modeling spatial diffusion patterns of COVID-19 stemming from Wuhan city to Hubei province. The methods include gravity and spatial auto-regression analyses. The local gravity model is derived from allometric scaling and global gravity model, and then the parameters of the local gravity model are estimated by observational data and linear regression. The main results are as below. The local gravity model based on power law decay can effectively describe the diffusion patterns and process of COVID-19 in Hubei Province, and the goodness of fit of the gravity model based on negative exponential decay to the observation data is not satisfactory. Further, the…
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