A stochastic SIR network epidemic model with preventive dropping of edges

TL;DR

This paper develops a stochastic SIR epidemic model on networks where susceptible individuals can drop edges to infectious neighbors, providing law of large numbers and central limit theorems for the epidemic's behavior as population size grows.

Contribution

It introduces an effective degree formulation for the SIR model with preventive edge dropping and derives asymptotic results, including a law of large numbers and a conjectured central limit theorem.

Findings

The model's basic reproduction number $R_0$ remains unchanged with edge dropping.

Probability of a major outbreak increases with edge dropping when $R_0>1$.

Asymptotic approximations are accurate even for moderate population sizes.

Abstract

A Markovian SIR (Susceptible-Infectious-Recovered) model is considered for the spread of an epidemic on a configuration model network, in which susceptible individuals may take preventive measures by dropping edges to infectious neighbours. An effective degree formulation of the model is used in conjunction with the theory of density dependent population processes to obtain a law of large numbers and a functional central limit theorem for the epidemic as the population size $N \to \infty$, assuming that the degrees of individuals are bounded. A central limit theorem is conjectured for the final size of the epidemic. The results are obtained for both the Molloy-Reed (in which the degrees of individuals are deterministic) and Newman-Strogatz-Watts (in which the degrees of individuals are independent and identically distributed) versions of the configuration model. The two versions yield the same limiting deterministic model but the asymptotic variances in the central limit theorema are greater in the Newman-Strogatz-Watts version. The basic reproduction number $R_0$ and the process of susceptible individuals in the limiting deterministic model, for the model with dropping of edges, are the same as for a corresponding SIR model without dropping of edges but an increased recovery rate, though, when $R_0>1$, the probability of a major outbreak is greater in the model with dropping of edges. The results are specialised to the model without dropping of edges to yield conjectured central limit theorems for the final size of Markovian SIR epidemics on configuration-model networks, and for the giant components of those networks. The theory is illustrated by numerical studies, which demonstrate that the asymptotic approximations are good, even for moderate $N$.