Epidemic spread, parameter sensitivity and vaccination strategies on a random graph with overlapping communities

TL;DR

This paper investigates how community structures in a random graph influence epidemic spread, sensitivity to parameters, and evaluates vaccination strategies to optimize protection of vulnerable groups.

Contribution

It introduces a model with overlapping communities and analyzes the impact of parameters and vaccination strategies on epidemic control.

Findings

Community structure significantly affects epidemic dynamics.

Vaccination strategies vary in effectiveness depending on network information.

Sensitivity analysis identifies key parameters influencing spread.

Abstract

Our main goal is to examine the role of communities in epidemic spread in a random graph model. More precisely, we consider a random graph model which consists of overlapping complete graphs, representing households, workplaces, school classes, and which also has a simple geometric structure. We study the model's sensitivity to infection parameters and other tunable parameters of the model, which might be helpful in finding efficient social distancing strategies. We also quantitatively compare different vaccination strategies to see which order is the best to defend the most vulnerable groups or the population in general, and how important it is to gather and use information on the position of infected individuals in the network.