Co-circulation of infectious diseases on networks

TL;DR

This paper develops a low-dimensional ODE model for the co-circulation of multiple infectious diseases on static networks, capturing how initial conditions influence epidemic dynamics and allowing adaptation to other spreading phenomena.

Contribution

It introduces a novel, simplified ODE framework for modeling multiple diseases spreading simultaneously on networks with immunity and behavior change considerations.

Findings

The model accurately captures the global dynamics of co-circulating diseases.

Initial conditions significantly influence epidemic outcomes.

The framework can be adapted to other spreading processes like rumors or technology adoption.

Abstract

We consider multiple diseases spreading in a static Configuration Model network. We make standard assumptions that infection transmits from neighbor to neighbor at a disease-specific rate and infected individuals recover at a disease-specific rate. Infection by one disease confers immediate and permanent immunity to infection by any disease. Under these assumptions, we find a simple, low-dimensional ordinary differential equations model which captures the global dynamics of the infection. The dynamics depend strongly on initial conditions. Although we motivate this article with infectious disease, the model may be adapted to the spread of other infectious agents such as competing political beliefs, rumors, or adoption of new technologies if these are influenced by contacts. As an example, we demonstrate how to model an infectious disease which can be prevented by a behavior change.