Related Experiment Video
Updated: Nov 28, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Regional infectious risk prediction of COVID-19 based on geo-spatial data
Xuewei Cheng1, Zhaozhou Han2, Badamasi Abba1
1School of Mathematics and Statistics, Central South University, China, Changsha, Hunan, China.
Abstract:
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.
Related Concept Videos
Steps in Outbreak Investigation
Selected Data About Geographic Locations
Applications of GIS: Disaster Management and Emergency Response
Principles of Disease Surveillance
Levels of Use of a GIS
Manipulation and Analysis

