Epidemic Spreading in Random Rectangular Networks
Ernesto Estrada, Sandro Meloni, Matthew Sheerin, Yamir Moreno

TL;DR
This paper models disease spread on spatial networks by generalizing random geometric graphs to rectangular shapes, showing that elongated plots increase epidemic thresholds and reduce disease propagation.
Contribution
It introduces a new rectangular random geometric graph model and analytically and numerically demonstrates how shape affects epidemic thresholds in spatial disease models.
Findings
Elongated plots increase epidemic threshold, making networks more resilient.
Shape influences disease spread more than area alone.
Results align with empirical observations in plant disease propagation.
Abstract
The use of network theory to model disease propagation on populations introduces important elements of reality to the classical epidemiological models. The use of random geometric graphs (RGG) is one of such network models that allows for the consideration of spatial properties on disease propagation. In certain real-world scenarios -like in the analysis of a disease propagating through plants- the shape of the plots and fields where the host of the disease is located may play a fundamental role on the propagation dynamics. Here we consider a generalization of the RGG to account for the variation of the shape of the plots/fields where the hosts of a disease are allocated. We consider a disease propagation taking place on the nodes of a random rectangular graph (RRG) and we consider a lower bound for the epidemic threshold of a Susceptible-Infected-Susceptible (SIS) or…
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