Monotonic and Non-Monotonic Epidemiological Models on Networks

TL;DR

This paper explores how contact networks influence epidemic spread, introducing a non-monotonic model where increased transmissibility can reduce outbreak size, suggesting new intervention strategies for public health.

Contribution

It provides an elementary proof of monotonicity in epidemic size relative to transmission probability and introduces the 2FleeSIR model demonstrating non-monotonic behavior.

Findings

Increasing transmission probability generally increases outbreak size in traditional models.

The 2FleeSIR model shows that higher transmissibility can decrease outbreak size in certain networks.

Public health interventions can have counterintuitive effects in non-monotonic epidemic models.

Abstract

Contact networks can significantly change the course of epidemics, affecting the rate of new infections and the mean size of an outbreak. Despite this dependence, some characteristics of epidemics are not contingent on the contact network and are probably predictable based only on the pathogen. Here we consider SIR-like pathogens and give an elementary proof that for any network increasing the probability of transmission increases the mean outbreak size. We also introduce a simple model, termed 2FleeSIR, in which susceptibles protect themselves by avoiding contacts with infectees. The 2FleeSIR model is non-monotonic: for some networks, increasing transmissibility actually decreases the final extent. The dynamics of 2FleeSIR are fundamentally different from SIR because 2FleeSIR exhibits no outbreak transition in densely-connected networks. We show that in non-monotonic epidemics, public health officials might be able to intervene in a fundamentally new way to change the network so as to control the effect of unexpectedly-high virulence. However, interventions that decrease transmissibility might actually cause more people to become infected.