Eight challenges for network epidemic models

TLDR

Networks provide a powerful framework for studying infectious disease spread, yet their high dimensionality makes modeling transmission mathematically and computationally difficult, leaving many basic questions unanswered. The authors call for a more general theory to clarify how network structure influences infection dynamics and control strategies. They outline a set of challenges that delineate key research directions in network epidemic modeling.

Abstract

Networks offer a fertile framework for studying the spread of infection in human and animal populations. However, owing to the inherent high-dimensionality of networks themselves, modelling transmission through networks is mathematically and computationally challenging. Even the simplest network epidemic models present unanswered questions. Attempts to improve the practical usefulness of network models by including realistic features of contact networks and of host–pathogen biology (e.g. waning immunity) have made some progress, but robust analytical results remain scarce. A more general theory is needed to understand the impact of network structure on the dynamics and control of infection. Here we identify a set of challenges that provide scope for active research in the field of network epidemic models.