Regional infectious risk prediction of COVID-19 based on geo-spatial data

被引：4
作者：
Cheng, Xuewei [1 ]
Han, Zhaozhou [2 ]
Abba, Badamasi [1 ]
Wang, Hong [1 ]
机构：
[1] Cent South Univ, Sch Math & Stat, Changsha, Hunan, Peoples R China
[2] Jinan Univ, Sch Econ, Guangzhou, Guangdong, Peoples R China
来源：
PEERJ | 2020年 / 8卷
关键词：
Risk prediction; Migration index; Geography-economy matrix; Geo-spatial data; GEOGRAPHIC INFORMATION-SYSTEMS; CORONAVIRUS DISEASE 2019; EUCLIDEAN DISTANCE; CHINA; DIAGNOSIS; OUTBREAK; WUHAN; SPREAD;
D O I：
10.7717/peerj.10139
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
After the first confirmed case of the novel coronavirus disease (COVID-19) was found, it is of considerable significance to divide the risk levels of various provinces or provincial municipalities in Mainland China and predict the spatial distribution characteristics of infectious diseases. In this paper, we predict the epidemic risk of each province based on geographical proximity information, spatial inverse distance information, economic distance and Baidu migration index. A simulation study revealed that the information based on geographical economy matrix and migration index could well predict the spatial spread of the epidemic. The results reveal that the accuracy rate of the prediction is over 87.10% with a rank difference of 3.1. The results based on prior information will guide government agencies and medical and health institutions to implement responses to major public health emergencies when facing the epidemic situation.
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页数：24
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