Exploring the limits of the law of mass action in the mean field description of epidemics on Erd\"os-R\'enyi networks

TL;DR

This paper investigates the applicability of the law of mass action in mean field models of epidemics on Erd"os-Rényi networks, showing it works well at low connectivity with adjusted contagion rates.

Contribution

It demonstrates that mean field differential equations can accurately describe epidemic dynamics on Erd"os-Rényi networks at low average connectivity by adjusting the contagion rate.

Findings

Mean field approach is valid for low average connectivity networks.

Adjusted contagion rate improves the model's accuracy.

Critical thresholds depend on network connectivity.

Abstract

The manner epidemics occurs in a social network depends on various elements, with two of the most influential being the relationships among individuals in the population and the mechanism of transmission. In this paper, we assume that the social network has a homogeneous random topology of Erd\"os-R\'enyi type. Regarding the contagion process, we assume that the probability of infection is proportional to the proportion of infected neighbours. We consider a constant population, whose individuals are the nodes of the social network, formed by two variable subpopulations: Susceptible and Infected (SI model). We simulate the epidemics on this random network and study whether the average dynamics can be described using a mean field approach in terms of Differential Equations, employing the law of mass action. We show that a macroscopic description could be applied for low average connectivity, adjusting the value of the contagion rate in a precise function. This dependence is illustrated by calculating the transient times for each connectivity. This study contributes valuable insights into the interplay between network connectivity, contagion dynamics, and the applicability of mean-field approximations. The delineation of critical thresholds and the distinctive behaviour at lower connectivity enable a deeper understanding of epidemic dynamics.