Space-time dependence of corona virus (COVID-19) outbreak

TL;DR

This paper investigates the spatial and temporal patterns of COVID-19 spread globally and in China, revealing an approximate inverse square law relationship between distance from the epicenter and case numbers using epidemic modeling and correlation analysis.

Contribution

It applies a Susceptible-Infected-Removed model on Euclidean networks and analyzes spatial correlations, providing new insights into COVID-19's spread dynamics.

Findings

Inverse square law approximately holds for case distribution

Spatial correlation between distance and case numbers identified

Modeling approach offers a framework for epidemic spread analysis

Abstract

We analyse the data for the global corona virus (COVID-19) outbreak using the results of a previously studied Susceptible-Infected-Removed (SIR) model of epidemic spreading on Euclidean networks. We also directly study the correlation of the distance from the epicenter and the number of cases. An inverse square law is seen to exist approximately. The studies are made for China and the rest of the world separately.