The interplay of network structure and correlated infectious traits in epidemic models

TL;DR

This paper develops a mathematical framework to analyze how population structure and correlated traits like susceptibility and transmissibility influence epidemic spread, extending traditional SIR models.

Contribution

It introduces a joint distribution-based model for subgroups with correlated susceptibility and transmissibility, providing analytical expressions for the basic reproduction number.

Findings

Derived analytical expressions for R0 considering population heterogeneity.

Validated theoretical results with numerical simulations.

Explored implications for social interventions in epidemic control.

Abstract

Individual contributions to the spread of an epidemic vary widely due to an individual's location in a social network and their intrinsic ability to spread or contract diseases. While the effect of heterogeneous population structure and infection rates is well-understood, less studied is the impact of population-level covariance between susceptibility and transmissibility, despite empirical evidence showing that both susceptibility and transmission vary across individuals. We introduce a mathematical modeling framework incorporating population subgroups, each with its own joint distribution of susceptibility and transmissibility. We apply this framework to the susceptible-infected-recovered (SIR) model to examine the effect of community structure and degree heterogeneity. We derive analytical expressions for the basic reproduction number, which, when reduced, corroborates prior results and validate these results with numerical simulations. We pair these estimates with simulations exploring first, the temporal dynamics of this model with the homogeneous SIR model, and second, implications for effective social intervention. This analysis provides a foundation for future studies exploring the interplay between structural and dynamical heterogeneity in infectious disease transmission.