Exact deterministic representation of Markovian SIR epidemics on networks with and without loops

TL;DR

This paper extends the exact deterministic modeling of Markovian SIR epidemics from tree-like networks to certain loopy networks by introducing new closures based on network structure, enabling accurate and computationally feasible epidemic predictions.

Contribution

The paper introduces novel closure techniques for networks with loops, generalizing previous tree-based models, and provides explicit ODE formulations for realistic networks.

Findings

Closures with intact loops are exact for certain network classes.

The method yields simplified, numerically solvable ODE systems.

Application to realistic networks shows good agreement with simulations.

Abstract

In a previous paper Sharkey et al. [13] proved the exactness of closures at the level of triples for Markovian SIR (susceptible-infected-removed) dynamics on tree-like networks. This resulted in a deterministic representation of the epidemic dynamics on the network that can be numerically evaluated. In this paper, we extend this modelling framework to certain classes of networks exhibiting loops. We show that closures where the loops are kept intact are exact, and lead to a simplified and numerically solvable system of ODEs (ordinary-differential-equations). The findings of the paper lead us to a generalisation of closures that are based on partitioning the network around nodes that are cut-vertices (i.e. the removal of such a node leads to the network breaking down into at least two disjointed components or subnetworks). Exploiting this structural property of the network yields some natural closures, where the evolution of a particular state can typically be exactly given in terms of the corresponding or projected sates on the subnetworks and the cut-vertex. A byproduct of this analysis is an alternative probabilistic proof of the exactness of the closures for tree-like networks presented in Sharkey et al. [13]. In this paper we also elaborate on how the main result can be applied to more realistic networks, for which we write down the ODEs explicitly and compare output from these to results from simulation. Furthermore, we give a general, recipe-like method of how to apply the reduction by closures technique for arbitrary networks, and give an upper bound on the maximum number of equations needed for an exact representation.