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

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.

Forecasting the COVID-19 transmission in Italy based on the minimum spanning tree of dynamic region network

“…The COVID-19 pandemic has sparked an intense debate about the factors underlying the dynamics of the outbreak ( Pacheco Coelho et al, 2020 ; Pequeno et al, 2020 ). Meanwhile, the study of mathematical models of epidemiology is helpful to understand the dynamics of epidemics, being an important tool to evaluate the potential effects of preventive and controlled measures, especially when their characteristics are still unclear ( Chatterjee et al, 2020 ; Khan et al, 2019 ; Cheng et al, 2020 ). Under such circumstances, by exploring the dynamical information from region networks and time-series data, we employed a combined model, the minimum-spanning-tree-based dynamical network marker (MST-DNM) ( Yang et al, 2020 ), to quantitatively describe the dynamics of COVID-19 transmission and thus identify the early-warning signal of a new wave of COVID-19 pandemic in Italy.…”

Espaço E Covid-19:

“…KIM et al, 2021;SCARPONE et al, 2020;KWOK et al, 2021;BUJA et al, 2020;VAZ, 2021). Como exemplo, tem-se a pesquisa de Lenzen et al (2020) BHERWANI et al, 2021;ZHANG et al, 2020;SANGIORGIO;PARISI, 2020;GIANQUINTIERI et al, 2020;POURGHASEMI et al, 2020;SHAW et al, 2021;KARAIVANOV, 2020;PAPASTEFANOPOULOS;VICTOR OKHUESE, 2020;LINARDATOS;KOTSIANTIS, 2020;CHENG et al, 2020;CHINAZZI et al, 2020;KONICEK et al, 2020;JIANG et al, 2020;PANG et al, 2021;ZHOU et al, 2020).…”