Dynamics of multi-stage infections on networks

TL;DR

This paper models multi-stage infectious disease dynamics on networks, deriving analytical expressions for epidemic size and thresholds, and demonstrates how multiple stages influence outbreak speed and severity through simulations.

Contribution

It introduces a multi-stage infection model on networks with analytical and simulation analysis, highlighting the impact of disease stages on epidemic dynamics.

Findings

More disease stages lead to faster outbreaks.

Higher stages increase peak prevalence.

Agreement between models and simulations is excellent.

Abstract

This paper investigates the dynamics of infectious diseases with a non-exponentially distributed infectious period. This is achieved by considering a multi-stage infection model on networks. Using pairwise approximation with a standard closure, a number of important characteristics of disease dynamics are derived analytically, including the final size of an epidemic and a threshold for epidemic outbreaks, and it is shown how these quantities depend on disease characteristics, as well as the number of disease stages. Stochastic simulations of dynamics on networks are performed and compared to the results of pairwise models for several realistic examples of infectious diseases to illustrate the role played by the number of stages in the disease dynamics. These results show that a higher number of disease stages results in faster epidemic outbreaks with a higher peak prevalence and a larger final size of the epidemic. The agreement between the pairwise and simulation methods is excellent in the cases we consider.