# Exploring the threshold of epidemic spreading for a stochastic SIR model with local and global contacts

## TL;DR

This paper investigates the epidemic threshold in a spatial stochastic SIR model with local and global contacts, deriving an analytical threshold estimate and analyzing outbreak dynamics at the threshold.

## Contribution

It provides an analytical expression for the epidemic threshold in a spatial stochastic SIR model with mixed contact types, validated by stochastic simulations.

## Key findings

- The threshold for epidemic spread depends on the ratio of transmission to recovery rates.
- The analytical threshold estimate closely matches stochastic simulation results.
- Outbreak size distributions at the threshold follow a power-law decay with exponent -3/2.

## Abstract

The spread of an epidemic process is considered in the context of a spatial SIR stochastic model that includes a parameter $0\le p\le 1$ that assigns weights $p$ and $1- p$ to global and local infective contacts respectively. The model was previously studied by other authors in different contexts. In this work we characterized the behavior of the system around the threshold for epidemic spreading. We first used a deterministic approximation of the stochastic model and checked the existence of a threshold value of $p$ for exponential epidemic spread. An analytical expression, which defines a function of the quotient $\alpha$ between the transmission and recovery rates, is obtained to approximate this threshold. We then performed different analyses based on intensive stochastic simulations and found that this expression is also a good estimate for a similar threshold value of $p$ obtained in the stochastic model. The dynamics of the average number of infected individuals and the average size of outbreaks show a behavior across the threshold that is well described by the deterministic approximation. The distributions of the outbreak sizes at the threshold present common features for all the cases considered corresponding to different values of $\alpha>1$. These features are otherwise already known to hold for the standard stochastic SIR model at its threshold, $\alpha=1$: (i) the probability of having an outbreak of size $n$ goes asymptotically as $n^{-3/2}$ for an infinite system, (ii) the maximal size of an outbreak scales as $N^{2/3}$ for a finite system of size $N$.