Edge-Based Compartmental Modeling for Infectious Disease Spread Part III: Disease and Population Structure

TL;DR

This paper extends edge-based compartmental models for infectious diseases to incorporate complex population structures, disease natural histories, and behaviors, enhancing their realism and applicability in static and dynamic networks.

Contribution

It introduces methods to handle population heterogeneity, complex disease progression, and behaviors like serosorting within the edge-based modeling framework.

Findings

Models can incorporate demographic and behavioral heterogeneity.

Extensions handle complex disease natural histories.

Framework is adaptable to various network dynamics.

Abstract

We consider the edge-based compartmental models for infectious disease spread introduced in Part I. These models allow us to consider standard SIR diseases spreading in random populations. In this paper we show how to handle deviations of the disease or population from the simplistic assumptions of Part I. We allow the population to have structure due to effects such as demographic detail or multiple types of risk behavior the disease to have more complicated natural history. We introduce these modifications in the static network context, though it is straightforward to incorporate them into dynamic networks. We also consider serosorting, which requires using the dynamic network models. The basic methods we use to derive these generalizations are widely applicable, and so it is straightforward to introduce many other generalizations not considered here.